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# Proof for Equation 5
Proof. The transformation is motivated by Xie et al. [2021] and we apply it to the analysis of RAG:
|p(xi|R, x1:i−1) = ∫∫ p(xi|R, x1:i−1, z)p(z|R, x1:i−1) dz|(20)|
|---|---|
|p(xi|R, x1:i−1) = ∫ p(xi|R, x1:i−1, z) p(R, x1:i−1|z)p(z)dz|(21)|
|p(xi|R, x1:i−1) ∝ ∫ p(xi|R, x1:i−1, z)p(R, x1:i−1|z)p... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
[2023], we make the following assumptions that:
13 | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
See downlink multi-user multiple input, multiple output (DL-MU-MIMO) (in 3.2). (0.343)|
|unknown_definition_2:NOTE See IETF RFC 3610. (0.376)|unknown_definition_13: NOTE For the purposes of this Standard, there is at most one portal in a given extended service set s (ESS s) infrastructure. In an implementation, a singl... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
# Assumption 1
All tokens can be predicted, which means that for every token x, there is some hidden state h lower bounds it that p(x|h, z∗) > c1 > 0.
# Assumption 2
Delimiter is an important distinguishing signal between each passage r in the retrieved texts R. For any delimiter hidden state hd and other hidden sta... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
When z = z∗,̸ exp(r(z∗)) = 1, which means that latent variable model concentrates more on z∗ sampled from retrieved texts. As r(z) decreases, the proportion of retrieved knowledge in becomes larger and larger in fusion. | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
• The more detriment outweigh benefit, r(z) → +∞ and exp(r(z)) → +∞ for all z = z∗ and when z = z∗, exp(r(z∗)) = 1. This indicates that concepts z sampled from LLMs’ space contribute more and more than z∗ sampled from retrieved texts as r(z) increases.
Proof for Theorem 1
Proof. Recapping the Equation 2 that describe... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
We use 1-norm to calculate the difference between p(xi|R, x1:i−1) and pR(xi|x1:i−1), which can be formalized as:
∥p(xi|R, x1:i−1) − pR(xi|x1:i−1)∥1 = ∥Φ + βW B − W u∥1.
(45)
Then, according to the triangle inequality of 1-norm, the difference between p(xi|R, x1:i−1) and pR(xi|x1:i−1) is bounded by:
∥Φ∥1 − ∥βW B − W... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
Then:
∥βB − u∥1 = 2T V (pR(·), βp(·|R, z∗))∗)) TV is Total Variation Distance. (53)
≤ 2βT V (pR(·), p(·|R, z (54)
≤p2KL(pR(·)∥p(·|R, z∗)) Pinsker’s Inequality. (55)
≤p2KL(pR(·)∥p(·|z∗)) (56)
in which r is the passage in R, KL(pR(r)∥p(r|z∗∗)),≈p2KL(pR(r)∥p(r|z (57)
) is actually the detrimentin Equation 9. Recappi... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
(64)
16 | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
Now the Theorem 1 has been proven.
Proof for Theorem 2
In this section, we try to prove that the gap between values of benefit and detriment is approximately positively correlated with the similarity (1) between p(xi|R, x1:i−1) and pR(xi|x1:i−1). To achieve this, we can start from Equation 60 to prove that the gap be... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
Recapping Equation 5 and 9:
|Z|p(xi|R, x1:i−1) =|Z−{z∗}p(xi|R, x1:i−1, z)p(z|R, x1:i−1) dz+ p(xi|R, x1:i−1, z∗)p(z∗|R, x1:i−1).{z } | denote as Λ{z )|
|---|---|
|Z|= p(xi|R, x1:i−1, z)p(z|R, x1:i−1) dz|
|ZZ|= p(xi|R, x1:i−1, z) p(R, x1:i−1|z)p(z)dz|
|ZZ|∝ p(xi|R, x1:i−1, z)p(R, x1:i−1|z)p(z) dz, p(R, x1:i−1) is a cons... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
and the upper bound of Equation 60 is:∥Φ∥1 +√2Υ ∝ exp(−(Ω − Υ)) + √2Υ (71)
Due to both Ω and Υ being variables, analyzing the result of subtraction between Ω and Υ under
their simultaneous changes is complex. Therefore, we use the “Separation of variables“ to si... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
On behalf of the analysis above, we can derve that both lower and upper bounds in Equation 60 are
approximately negatively correlated with the gap between values of benefit and detriment. Therefore,
the difference D between p(xi|R, x1:i−1) and pR(xi|x1:i−1) is approximately negatively correlated1
with the gap between v... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
bridge-topic into a claim set. We restrict the claim Category Avg. Tokens Entry Count set to have at least two claims but no more than four technology 2262.3 172 claims. For each type of query, we feed the claim entertainment 2084.3 114 set to GPT-4 and prompt it with an instruction to sports 2030.6 211 generate a quer... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
|Query ID|Query|Expected answer|Search for paragraph|Search by sentence retrieve paragraph|Observations|
|---|---|---|---|---|---|
|F1|What do the values of RAW Group Indication subfield in RPS element indicate?|The RAW Group Indication subfield indicates whether the RAW Group subfield is present in the RAW Assignment ... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
Recapping Equation 2 that z∗ is sampled from retrieved texts and z is sampled from LLMs’ pre-trained knowledge, Equation 78 indicates that the knowledge of retrieved texts has been involved in LLLs’ pre-trained knowledge, so:
p(xi|x1:i−1) = pR(xi|x1:i−1), (79)
then:
∥p(xi|R, x1:i−1) − p(xi∥x1:i−1)∥1 = ∥p(xi|R, x1:i−... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
[2022]. We prove that in this perspective, the distribution of texts in context drives the learning even without explicit input-output supervision. Therefore, the distribution of unsupervised retrieved texts in RAG, which is actually the distribution of context for query, can also drives the learning. Then we can prove... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
# In-context Learning Equations:
1. \(f_{ICL}(q) = Attn(V, K, q)\) (89)
2. \(= WV [B′ : B]softmax(WK [B′ : B])^T q/\sqrt{d}\) (90)
3. To simplify qualitative analysis, the standard attention is estimated as relaxed linear attention by eliminating the softmax function and the scaling factor:
\(f_{ICL}(q) \approx WV ... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
\(= \int_{-z^*} p(xi|R, x1:i−1, z)p(z|R, x1:i−1) dz + p(xi|R, x1:i−1, z^*)p(z^*|R, x1:i−1)\) (104)
3. \(∝ \int p(xi|R, x1:i−1, z)p(R, x1:i−1|z)p(z) dz\) (105)
4. \(= \int \int p(xi|R, x1:i−1, z)exp(r(z))p(z) dz, r(z) = log p(R, x1:i−1|z^*)p(R, x1:i−1|z)\) (106) | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
# Experimental details
# Baselines
For primary experiment that needs methods to determine the value order between benefit and detriment for each token, it is actually a binary classification task (benefit outweigh detriment or not). The mainstream methods in this area are detecting and comparing the degree of halluci... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
Below we will describe in detail how we apply these baselines to this task.
# Logprobs
Logprobs can indicate the confidence for LLMs in generating the tokensKuhn et al. [2023]. We use the value order between top-1 log-probability of the tokens output by pure LLM and RAG to determine the value order between benefit an... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
# Consistency-Semantic
Chen et al. [2024]. We follow Chen et al. [2024] to use EigenScore to calculate the semantic similarity among hidden states of tokens in multiple generations and use it as the consistency score.
For open-domain Q&A under practical autoregressive generation setting, baselines for this include th... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
This method uses a retrieval evaluator to assess the correctness of retrieved texts trigger different actions based on the evaluation results. One of the actions is using additional google search API for web search, which is unfair for baselines and our method. So we remove this action and use its knowledge refinement ... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
RetRobust. This method fine-tunes LLMs to properly leverage retrieved passages with a mix of relevant and irrelevant contexts Yoran et al. [2024].
INFO-RAG. This method uses unsupervised method to make LLMs learn to use the retrieved texts robustly. It enables LLMs to judge the correctness of the retrieved texts, extr... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
# Implementation details
All models are run on a V100 GPU with Pytorch Paszke et al. [2019] and accelerated by DeepSpeed3.
As for retrieval for RAG, we follow Xu et al. [2023, 2024a] to use ColBERTv2 Santhanam et al. [2021], an excellent generalizable model as the retriever, and use Wikipedia consisting of 21,015,324... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
When the RAW Group Indication subfield is equal to 1, the RAW Group subfield is present. The RAW Group Indication subfield in the first RAW assignment is set to 0 to indicate the RAW group in the first RAW assignment is the same as the range of AIDs in all the TIM bitmaps in the S1G Beacon frame. When the RAW is a non-... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
# Question: Who is the book of Galatians written to?
|Pure LLM:|It was written by the Apostle Peter to the churches in Galatia, a region of present-day Turkey.|
|---|---|
|RAG:|It was written by the Apostle Paul to the churches in Corinth, a region of present-day Turkey.|
|Output:|It was written by the Apostle Paul to... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
Pure LLM and RAG generate the texts in parallel at token level. At the step that pure LLM and RAG generate the different tokens, X-RAG use our theoretical results in Theorem 3 to compare the benefit and detriment. If benefit is greater than detriment, the token from RAG is selected, otherwise, the token from pure LLM i... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
# arXiv:2311.09476v2 [cs.CL] 31 Mar 2024
|ARES: An Automated Evaluation Framework for Retrieval-AugmentedGeneration Systems|
|---|
|Jon Saad-Falcon|Omar Khattab|
|Stanford University *|Stanford University|
|jonsaadfalcon@stanford.edu|okhattab@stanford.edu|
|Christopher Potts|Matei Zaharia|
|Stanford University|Databri... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
Unfortunately, both of these strategies demand high expertise and impose considerable annotation costs.
Model-based evaluation is an inexpensive strategy to test generative output quality (Zheng et al., 2023). For instance, the open-source RAGAS framework (James and Es, 2023) prompts an LM for evaluating the relevance... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
2023). Given a corpus of documents and a RAG system, ARES reports three evaluation scores: context relevance (is the retrieved information pertinent to the test question), answer faithfulness (is the response generated by the language model properly grounded in the retrieved context), and answer relevance (is the respo... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
RAG system finds relevant contexts and generates answers that are both faithful and relevant.
Many existing RAG evaluation frameworks require substantial human annotations for scoring. ARES significantly improves data efficiency during evaluation by only requiring three inputs: an in-domain passage set, a human prefer... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
This is essential for rapid deployment in new settings, where it is difficult to build a traditional benchmark dataset from scratch. Early attempts at this use LLMs out of the box, as in MT-Bench and Chatbot Arena (Zheng et al., 2023). AutoCalibrate (Liu et al., 2023b) seeks to align an LLM-judge with human preferences... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
RAGAS is based on a handful of heuristic hand-written prompts. These offer little adaptability to new RAG evaluation set. | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
# ARES
ARES proceeds in three stages (Figure 1). There are three required inputs: an in-domain passage set, a human preference validation set of approximately 150 annotated datapoints (or more), and few-shot examples of in-domain queries and answers (five or more examples), which are used for prompting LLMs in synthet... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
2. Answer Faithfulness: Is the answer generated faithful to the retrieved passage, or does it contain hallucinated or extrapolated statements beyond the passage?
3. | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
|Query ID|Query|Expected answer|Generated response – similarity by paragraph|Generated Response – similarity by sentence, retrieve paragraph|Observations|
|---|---|---|---|---|---|
|F1|What do the values of RAW Group Indication subfield in RPS element indicate?|The RAW Group Indication subfield indicates whether the RA... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
Answer Relevance: Is the answer generated relevant given the query and retrieved passage?
For each metric, a separate LLM with a binary classifier head is fine-tuned to classify positive and negative examples. For each concatenated query-document-answer, a single LLM judge must classify the triple as positive or negat... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
Step #1: LLM Generation of Synthetic Dataset: Generate synthetic queries and answers from in-domain passages
Step #2: Preparing LLM Judges: Train LLM judges with synthetic data
Step #3: Ranking RAG Systems with Confidence Intervals: Use LLM judges to evaluate RAG systems with PPI human labels
Figure 1: Overview of A... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
In principle, we could simply report these average scores as quality metrics for each RAG system. However, these scores reflect entirely unlabeled data with predictions from a synthetically-trained LLM judge, and hence they may not be entirely accurate. As an extreme alternative, we could use just the small human prefe... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
With the accuracy confidence interval for each component of the RAG, we find the midpoint of each confidence interval and use the midpoints to rank the RAG systems. With our ranking, we can compare different RAG systems, as well as different configurations of the same RAG system, to find the best-performing approach fo... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
# Experiments
# Models
For our fine-tuned judges, ARES relies on generating cheap but quality synthetic queries and answers using LLMs. For generating our synthetic datasets, we use FLAN-T5 XXL (Chung et al., 2022). We selected DeBERTa-v3-Large (He et al., 2021) for our fine-tuned LLM judge. Our fine-tuned LLM judges... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
. . | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
, 90.0%). Each split also represents a different mock RAG system. Since we know the success percentages of each dataset split, we know the appropriate ranking of each mock RAG system. This allows us to | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
test ARES success at both scoring and ranking the mock RAG systems appropriately across the three evaluation criteria.
4.3 Metrics
To calculate the correlation between the correct ranking and the ARES ranking, we use the Kendall rank correlation coefficient or Kendall’s τ :
τ = (# of concordant pairs) − (# of discor... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
To this end, we conducted two sets of experiments. | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
First, we | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
When the RAW Group Indication subfield is equal to 1, the RAW Group subfield is present in this RAW assignment. The RAW Group Indication subfield in the first RAW assignment is set to 0 to indicate the RAW group in the first RAW assignment is the same as the range of AIDs in all the TIM bitmaps in the S1G Beacon frame.... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
# ARES Ranking of Pseudo RAG Systems
| |NQ|HotpotQA|WoW|FEVER|MultiRC|ReCoRD|
|---|---|---|---|---|---|---|
| |C.R|A.R.|C.R|A.R.|C.R|A.R.|C.R|A.R.|C.R|A.R.|C.R|A.R.|
|Kendall’s Tau for Sampled Annotations|0.83|0.89|0.78|0.78|0.78|0.83|0.89|0.89|0.83|0.83|0.72|0.94|
|Kendall’s Tau for RAGAS|0.89|0.89|0.94|0.89|0.94|0.9... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
and A.R. in the table, respectively), we compare ARES
with our fine-tuned LLM judges against sampled annotations benchmark, RAGAS, and a few-shot GPT-3.5 judge.
For our sampled annotations, we gather 150 annotated datapoints from each mock RAG system and use those labels
to score the system. RAGAS also uses GPT-3.5 as ... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
We consider the answer gen | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
In Table 5, we found that ARES can reliably score and rank RAG systems in real-world applications, averaging a Kendall’s tau of 0.91 for context relevance and 0.97 for answer relevance. Compared to RAGAS, ARES is 0.16 higher for context relevance and 0.15 higher for answer relevance, on average. ARES also provided accu... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
NQ to ReCoRD).
In Table 6, we found that the fine-tuned LLM judges used in ARES proved successful in cross-domain applications. Across all settings, we found that LLM judges in ARES had strong generalizability, even when only using 300 datapoints in our human preference validation set for PPI. Furthermore, we found th... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
questions + passages to coding functions + documentation), and switching from retrieving text to extraction of entities, webpages, or citations.
To test cross-lingual transfer, we used the XGLUE datasets (Liang et al., 2020); a LLM judge fine-tuned on NQ achieved a Kendall’s tau of 0.33 over both context relevance and... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
logits in LLM judge prediction to improve PPI Alec Radford, Ilya Sutskever, and Dario Amodei. confidence intervals, and testing more sophisticated 2020. Language models are few-shot learners. LLMs as fine-tuned judges for ARES. Jiawei Chen, Hongyu Lin, Xianpei Han, and Le Sun. | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
2023. Benchmarking large language models in retrieval-augmented generation. arXiv preprint arXiv:2309.01431.
Limitations ARES relies on a small set of annotations in the human preference validation set (roughly 150-300 datapoints but more is better). These annotations often require an annotator familiar with the RAG s... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
2023. Proceedings of the Sixth Fact Extraction and VERification Workshop (FEVER). Association for Computational Linguistics, Dubrovnik, Croatia. Anastasios N. Angelopoulos, Stephen Bates, Clara Fanjiang, Michael I. Jordan, and Tijana Zrnic. 2023. Prediction-powered inference. Tom B. Brown, Benjamin Mann, Nick Ryder, Me... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
# Hamel Husain, Ho-Hsiang Wu, Tiferet Gazit, Miltiadis Allamanis, and Marc Brockschmidt
2019. Code-SearchNet challenge: Evaluating the state of semantic code search. arXiv preprint arXiv:1909.09436.
# Gautier Izacard, Patrick Lewis, Maria Lomeli, Lucas Hosseini, Fabio Petroni, Timo Schick, Jane Dwivedi-Yu, Armand Jou... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
Prompt-RAG: Pioneering Vector Embedding-Free Retrieval-Augmented Generation in Niche Domains, Exemplified by Korean Medicine
Bongsu Kang1, Jundong Kim1, Tae-Rim Yun, Chang-Eop Kim1, 2, *
1Department of Physiology, College of Korean Medicine, Gachon University, Seongnam, Gyeonggi, Republic of Korea
2Department of Neuro... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
# Jithin James and Shahul Es
2023. Ragas: Evaluation framework for your retrieval augmented generation (rag) pipelines.
# Jeff Johnson, Matthijs Douze, and Hervé Jégou
2019. Billion-scale similarity search with GPUs. IEEE Transactions on Big Data, 7(3):535–547.
# Ehsan Kamalloo, Aref Jafari, Xinyu Zhang, Nandan Tha... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
# Kalpesh Krishna, Erin Bransom, Bailey Kuehl, Mohit Iyyer, Pradeep Dasigi, Arman Cohan, and Kyle Lo
2023. LongEval: Guidelines for human evaluation of faithfulness in long-form summarization. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 1650–166... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
Association for Computational Linguistics.
# Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, et al.
2019. Natural questions: a benchmark for question answering research. Transactions of the Association f... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
Udapdr: Unsupervised domain adaptation via llm prompting and distillation of rerankers. arXiv preprint arXiv:2303.00807.
| Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
David P Sander and Laura Dietz. 2021. Exam: How to evaluate retrieve-and-generate systems for users who do not (yet) know what they want. In DESIRES, pages 136–146.
Keshav Santhanam, Omar Khattab, Jon Saad-Falcon, Christopher Potts, and Matei Zaharia. | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
2022. COLBERTv2: Effective and efficient retrieval via lightweight late interaction. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 3715–3734, Seattle, United States. Association for Computational Linguistics.
Kur... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
Xiang Yue, Boshi Wang, Ziru Chen, Kai Zhang, Yu Su, and Huan Sun. 2023. Automatic evaluation of attribution by large language models.
Sheng Zhang, Xiaodong Liu, Jingjing Liu, Jianfeng Gao, Kevin Duh, and Benjamin Van Durme. 2018. Record: Bridging the gap between human and machine commonsense reading comprehension. arX... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
analyze the provided document and determine whether it is relevant for responding to the dialogue. In your evaluation, you should consider the content of the document and how it relates to the provided dialogue. Output your final verdict by strictly following this format: "[[Yes]]" if the document is relevant and "[[No... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
The answer also must not contradict information provided in the document. Output your final verdict by strictly following this format: "[[Yes]]" if the answer is faithful to the document and "[[No]]" if the answer is not faithful to the document. Do not provide any additional explanation for your decision.
Question: &... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
# For generating our synthetic answers, we use the following prompt for FLAN-T5 XXL:
Example #1
Query: <few-shot example here>
Document: <few-shot example here>
Answer: <few-shot example here>
Example #2
Query: <few-shot example here>
Document: <few-shot example here>
Answer: <few-shot... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
Its performance was assessed through a Question-Answering (QA) chatbot application, where responses were evaluated for relevance, readability, and informativeness. The results showed that Prompt-RAG outperformed existing models, including ChatGPT and conventional vector embedding-based RAGs, in terms of relevance and i... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
# RAG Systems Evaluation on NQ - Context Relevance
| |Facebook|BM25|BM25|BM25|OpenAI|OpenAI|OpenAI|CoIBERT|CoIBERT|CoIBERT|
|---|---|---|---|---|---|---|---|---|---|---|
| |RAG|MPT|GPT3.5|GPT4.0|MPT|GPT3.5|GPT4.0|MPT|GPT3.5|GPT4.0|
RAG Systems Evaluation on NQ - Context Relevance
# RAG Systems Evaluation on NQ - Ans... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
# Kendall’s Tau by Dataset
| |NQ|NQ|MultiRC|MultiRC|ReCoRD|ReCoRD|
|---|---|---|---|
|PPI Labeled Count|C.R.|A.R.|C.R.|A.R.|C.R.|A.R.|
|400|1.0|1.0|0.89|0.94|0.89|0.94|
|300|0.89|1.0|0.94|0.89|0.83|0.89|
|200|0.83|1.0|0.83|0.94|0.83|0.83|
|150|0.72|1.0|0.83|0.89|0.72|0.83|
|100|0.44|1.0|0.67|0.67|0.67|0.83|
|50|0.44|0... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
In the table, we define PPI range as the number of percentage points from the lower number to the upper number of the PPI confidence bounding. Additionally, we use the fine-tuned LLM judge (DeBERTa-v3-Large) for evaluation. | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
# Table 5: ARES Ranking on Real-World RAG Systems
| |Kendall’s Tau for Sampled Annotations|Kendall’s Tau for RAGAS|Kendall’s Tau for GPT-3.5 Judge|Kendall’s Tau for ARES LLM Judge|Kendall’s Tau for ARES|RAGAS Accuracy|GPT-3.5 Accuracy|ARES Accuracy|
|---|---|---|---|---|---|---|---|---|
|C.R.|0.73|0.73|0.73|0.82|0.82|... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
NQ and ReCoRD). For PPI, we used 300 labeled examples for our human preference validation set but also found that additional examples further improved the performance of ARES. Furthermore, we found that even in scenarios where the fine-tuned LLM judge’s accuracy significantly dropped out-of-domain (e.g. answer relevanc... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
|Query|Passage|Answer|Context Relevance|Answer Relevance|
|---|---|---|---|---|
|How can a ball that is not moving possess energy of position?|Mechanical energy is a combination of the energy of motion or position. This type of energy describes objects that are moving or could move. A moving ball can have energy from m... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
After killing Danvers the stepfather beats a suspicious guard named Ralph Smith to death with his own nightstick with only two strikes and takes his uniform, successfully sneaking out of the sanitarium. Checking into a hotel after robbing and murdering a traveling salesman the stepfather alters his appearance, takes th... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
# arXiv:2406.11147v2 [cs.SE] 19 Jun 2024
Vul-RAG: Enhancing LLM-based Vulnerability Detection via Knowledge-level RAG
Xueying Du
Fudan University
China
Jiayi Feng
Fudan University
China
Bihuan Chen
Fudan University
China
ABSTRACT
Vulnerability detection is essential for software quality assurance. In recent years, d... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
The results demonstrate that existing | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
Xueying Du, Geng Zheng, Kaixin Wang, Jiayi Feng, Wentai Deng, Mingwei Liu, Bihuan Chen, Xin Peng, Tao Ma, and Yiling Lou
Trained models have limited capabilities of capturing the high-level code semantics related to vulnerable behaviors in the given code. Technique. Inspired by the observation in our preliminary study... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
# Introduction
Retrieval-Augmented Generation (RAG) models combine a generative model with an information retrieval function, designed to overcome the inherent constraints of generative models. They integrate the robustness of a large language model (LLM) with the relevance and up-to-dateness of external information s... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
We construct a new benchmark PairVul that exclusively contains pairs of vulnerable code and similar-but-correct code.
- Preliminary Study. | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
We perform the first study to find that existing learning-based techniques have limited capabilities of understanding and capturing the vulnerability-related code semantics.
- Technique. We construct a vulnerability knowledge base based on the proposed multi-dimension knowledge representation, and propose a novel knowl... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
We evaluate Vul-RAG and find the usefulness of vulnerability knowledge generated by Vul-RAG for both automated and manual vulnerability detection.
# BACKGROUND
# CVE and CWE
Existing vulnerability classification systems, such as Common Vulnerabilities and Exposures (CVE) and Common Weakness Enumeration (CWE), provid... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
Enhancing LLM-based Vulnerability Detection via Knowledge-level RAG
detection, without modifying the original LLM parameters; the 3.1 Benchmark PairVul
latter updates LLM parameters by trained on vulnerability detection datasets, to learn the features of vulnerable code.
2.3 Retrieval-Augmented Generation
Retrieval... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
RAG has been widely used in various domains [25–28]. For example, RAG has been specialized to software engineering tasks such as code generation [27, 28], which retrieves the similar code from the code base and augments the prompt with the retrieved code for model inference.
3 PRELIMINARY STUDY
Although existing lear... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
# Table 1: Existing Benchmarks for Vulnerability Detection
|Benchmark|Time|Positive Number/Ratio|#CVE|Positive LOC|Negative LOC|Patched Code Included|Patched Code Verified|
|---|---|---|---|---|---|---|---|
|BigVul|2020|10,900 (5.78%)|3,285|73.47|23.83|N|/|
|Devign|2019|12,460 (45.61%)|/|54.50|49.53|N|/|
|ReVeal|2020|... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
Pair Num. 587|CVE Num. 145|Func. Pair Num. 267|
|CWE-476|CVE Num. 194|Func. Pair Num. 262|CVE Num. 60|Func. Pair Num. 89|
|CWE-362|CVE Num. 169|Func. Pair Num. 280|CVE Num. 81|Func. Pair Num. 121|
|CWE-119|CVE Num. 129|Func. Pair Num. 163|CVE Num. 42|Func. Pair Num. 53|
|CWE-787|CVE Num. 122|Func. Pair Num. 170|CVE Num... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
Pair Num. 62|
# Studied Baselines
We evaluate the following state-of-the-art (SOTA) vulnerability detection techniques on our benchmark PairVul:
- LLMAO: An LLM-based fault localization approach fine-tuning LLM (i.e., CodeGen), which has also been fine-tuned on the Devign dataset for vulnerability detection.
- LineV... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
In particular, the pairwise accuracy ranges from 0.01 to 0.10, indicating that existing learning-based techniques fail to capture the subtle difference between similar vulnerable code and non-vulnerable code. The observations imply that the learning-based models have limited capability of understanding the semantics re... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
similar-but-correct code. In particular, based on how developers
manually identify a vulnerability, understanding a vulnerability
often involves the code semantics from the three dimensions: (i)
the functionality the code is implementing, (ii) the causes for the
vulnerability, and (iii) the fixing solution for the ... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
However, the cost of fine-tuning, especially when it involves adjusting the entire or majority of parameters in LLM, has rapidly become expensive, thereby increasing the demand for alternative solutions.
To address these challenges, we propose a novel methodology: Prompt-RAG. This new approach to RAG eliminates the re... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
4.2.1 Vulnerability Knowledge Representation. Vul-RAG represents
the vulnerability knowledge of a CVE instance from three dimen-
sions: functional semantics, vulnerability causes, and fixing solu-
tions. Figure 3 exemplifies the three-dimension representation for
CVE-2022-38457. In this case, the vulnerable code ac... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
3...” | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
Xueying Du, Geng Zheng, Kaixin Wang, Jiayi Feng, Wentai Deng, Mingwei Liu, Bihuan Chen, Xin Peng, Tao Ma, and Yiling Lou
#CVE-2022-38457
A use-after-free (UAF) vulnerability was found in function 'vmw_cmd_res_check' in drivers/gpu/vmxgfx/vmxgfx_execbuf.c in Linux kernel's vmwgfx driver with device file '/dev/dri/rend... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
Detailed Behavior: 1. Look up a TTM base object using a key in a TTM object file. 2. Acquire a reference to the base object if found successfully. 3. Return the base object if a reference is acquired, otherwise return NULL.
Functional Semantics
Extraction Prompt
Abstract Vulnerability Description: Use of RCU read lo... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
The extracted knowledge might contain concrete variable names or types (e.g., “without &dev->ref initialization”), which can be abstracted into the more general description (e.g., “without proper reference counter initialization”).
Vul-RAG incorporates the following prompt to leverage LLMs for knowledge extraction, wh... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
Vul-RAG: Enhancing LLM-based Vulnerability Detection via Knowledge-level RAG
vulnerability knowledge base in a three-step retrieval process: query enhances LLMs with each retrieved knowledge item by sequentially generation, candidate knowledge retrieval, and candidate knowledge re-ranking.
Query Generation. | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
Instead of relying solely on the code as the retrieval query, Vul-RAG incorporates both the code and its functional semantics as a multi-dimension query. Firstly, Vul-RAG prompts LLMs to extract the functional semantics of the given code, as described in the knowledge base construction (Section 4.2.2). The abstract pur... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
Candidate Knowledge Re-ranking. We re-rank candidate knowledge items with the Reciprocal Rank Fusion (RRF) strategy. For each retrieved knowledge item k, we calculate its re-rank score by aggregating the reciprocal of its rank across all three query elements. If a knowledge item k is not retrieved by a particular query... | Provided the following context, I want to generate QA embedding pairs for each of my classes that are 3B and 7B LLM based on increasing complexity. For the 3B model, generate simple question-answer pairs, and for the 7B model, generate slightly more complex pairs. Please format the output in JSON as: {3B: {q1:, a1:}, {... |
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