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Xueying Du, Geng Zheng, Kaixin Wang, Jiayi Feng, Wentai Deng, Mingwei Liu, Bihuan Chen, Xin Peng, Tao Ma, and Yiling Lou
# RESULTS
# RQ1: Compared to SOTA techniques
In RQ1, we evaluate Vul-RAG with the same setting of our preliminary study (Section 3), including the same benchmark (i.e., PairVul), same metrics, 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:}, {... |
Nevertheless, the overall limited effectiveness of all techniques indicates that capturing the subtle semantic difference is very challenging, which calls for more awareness from the future work.
# RQ2: Compared to GPT-4-based techniques
RQ2 evaluates the usefulness of the knowledge-level RAG framework by comparing 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:}, {... |
ChatGPT’s scores on the Korean National Licensing Examination for Korean Medicine Doctors barely reached the passing threshold, underperforming in subjects unique to KM, especially Sasang constitutional medicine and public health & medicine-related law.(21) In this niche area, rich in specialized knowledge and distinct... | 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
Q: I want you to act as a vulnerability detection expert. Given the following code, please detect whether there is a vulnerability in the following code snippet:
static int da9150_charger_remove(struct platform_device *pdev)
{
struct da915... | 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:}, {... |
Hold pe HCI connection.
static void hci_log_link_complete_evt(struct hci_dev *hdev, struct sk_buff *skb)
{
...
BT_DBG("%s log_handle 0x%4.4x phy_handle 0x%2.2x status 0x%2.2x", hdev->name, le16_to_cpu(ev->handle), ev->phy_handle, ev->status);
hcon = hci_conn_hash_lookup_handle(hdev, ev->phy_handle);
if (!hcon)
return;... | 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 select 10 cases from the benchmark PairVul for a user study. Specifically, we randomly select two cases from each of the five CWE categories PairVul, including both true positive (i.e., genuinely vulnerable code snippets) and false positive (i.e., correct code snippets mistakenly predicted by Vul-RAG as vulnerable) ... | 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
# Generalizability:
The vulnerability knowledge maintain a degree of general applicability, eschewing overly specific descriptions that diminish its broad utility (e.g., narratives overly reliant o... | 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 reasons for false negatives are classified into three primary categories:
- Inaccurate Vulnerability Knowledge Descriptions. We observe that for 5 instances (26.3%), Vul-RAG successfully retrieves relevant vulnerability knowledge but fails to detect the vulnerability due to the imprecise knowledge 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:}, {... |
# THREATS TO VALIDITY
Threats in benchmarks. There might be potential data leakage issue between the vulnerability benchmark and the GPT-4 training data. Nevertheless, the substantial improvements of Vul-RAG over the basic GPT-4 can show the effectiveness of Vul-RAG is not simply due to data memorization. Threats in g... | 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:}, {... |
DeepDFA [ 3] uses a data flow analysis-guided graph learning framework to simulate data flow computation. For PLM-based vulnerability detection, VulBERTa [ 5] uses the RoBERTa model [22 ] as the encoder, while Linevul [6] uses attention scores for line-level prediction. | 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
LLM-based Vulnerability Detection. Wu et al. [42] and Zhou et al. [43] explore the effectiveness and limits of ChatGPT in software security applications; Gao et al. [44] build a comprehensive vulnerability benchmark VulBench to evaluate 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:}, {... |
[Online]. Available: Link|
|[3] B. Steenhoek, H. Gao, and W. Le|“Dataflow analysis-inspired deep learning for efficient vulnerability detection,” in Proceedings of the 46th IEEE/ACM International Conference on Software Engineering, ICSE 2024, Lisbon, Portugal, April 14-20, 2024. ACM, 2024, pp. | 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:}, {... |
16:1–16:13. | 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:}, {... |
Subsequently, we demonstrated Prompt-RAG's effectiveness in this context. A Question-Answering (QA) chatbot based on Prompt-RAG was built using KM-specific documents, and our model’s performance was compared with that of ChatGPT and conventional vector embedding-based RAG models. This study not only highlights the chal... | 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:}, {... |
[Online]. Available: Link|
|[4] Y. Mirsky, G. Macon, M. D. Brown, C. Yagemann, M. Pruett, E. Downing, S. Mertoguno, and W. Lee|“Vulchecker: Graph-based vulnerability localization in source code,” in 32nd USENIX Security Symposium, USENIX Security 2023, Anaheim, CA, USA, August 9-11, 2023, J. A. Calandrino and C. Tronco... | 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
[33] S. E. Robertson and S. Walker, “Some simple effective approximations to the 2-poisson model for probabilistic weighted retrieval,” in Proceedings of the 17th Annual International ACM-SIGIR Co... | 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:}, {... |
ACM/Springer, 1988, pp. 232–241. [Online]. Available: https://doi.org/10.1016/0306-4573(88)90021-0
[34] M. Çagatayli and E. Çelebi, “The effect of stemming and stop-word-removal on automatic text classification in turkish language,” in Neural Information Processing - 22nd International Conference, ICONIP 2015, Istanbu... | 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:}, {... |
[Online]. Available: https://doi.org/10.48550/arXiv.2303.08774
[39] (2023) Elasticsearch. [Online]. Available: https://github.com/elastic/elasticsearch
[40] R. Likert, “A technique for the measurement of attitudes.” Archives of psychology, 1932.
[41] M. Jimenez, M. Papadakis, and Y. L. Traon, “An empirical analysis ... | 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:2401.12599v1 [cs.AI] 23 Jan 2024
Revolutionizing Retrieval-Augmented Generation with Enhanced PDF Structure Recognition
Demiao LIN
chatdoc.com
# Abstract
With the rapid development of Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) has become a predominant method in the field of professi... | 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 process mirrors the typical cognitive process of encountering a problem, including consulting relevant references and subsequently deriving an answer. In this framework, the pivotal component is the accurate retrieval of pertinent information, which is critical for the efficacy of the RAG model.
However, the proc... | 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:}, {... |
# Example
Question: How to turn on full self-driving mode?
User
Embeddings
Knowledge Base
To enable Full Self-Driving (Beta), touch Controls > Autopilot > Autopilot Features > Full Self-Driving (Beta)
Documents
[Document Snippets] To enable Full Self-Driving (Beta), touch Controls ...
Combine content 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:}, {... |
The workflow of Retrieval-Augmented Generation (RAG).
Documents
Document Parsing & Chunking
|Title|Chunk 1|Store|
|---|---|---|
|Paragraph|Chunk 2| |
|Table|Image| |
| |Chunk 3| |
| |Chunk 4| |
Figure 2. The process of converting PDFs into retrievable contents.
- Document Parsing & Chunking. It involves extracting... | 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:}, {... |
# Tagged Documents
# Untagged Documents
We report the development...
Exam
# Figure 3. Two types of documents in the view of computers.
| 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:}, {... |
Paragraphs, tables, and charts, and then understood or memorized. However, computers perceive information as binary codes. From their perspective, as illustrated in Figure 3, documents can be categorized into two distinct types:
- Tagged Documents: Examples include Microsoft Word and HTML documents, which contain spec... | 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:}, {... |
Finally, we use two with "who," with the remainder incorporating a approaches to reassure the dataset quality. First, we small percentage of other interrogative words such manually review a subset sample of the generated as "when." Moreover, the number of evidence re- multi-hop queries, their corresponding evidence qui... | 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:}, {... |
# Design of Prompt-RAG
In this study, we introduce Prompt-RAG, a novel approach distinct from the conventional vector embedding-based RAG. Prompt-RAG consists of three steps: preprocessing, heading selection, and retrieval-augmented generation. The overall scheme of Prompt-RAG might seem similar to that of conventiona... | 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:}, {... |
They do not store any structural information of the document, like tables or paragraphs. Thus, untagged documents are only for human e-reading, but are unreadable by machines. This becomes evident when attempting to copy a table from a PDF into MS Word, where the original structure of the table is often completely lost... | 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:}, {... |
# Case 1: PyPDF
Original Page:Chunking Result:
Year ended March 31, 2021We believe that adjusted EBITDA, | 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:}, {... |
into multiple lines (e.g. the cell “China commerce(1)”) and some adjacent cells may be arranged in one line (e.g. the third to the fifth cells in the second line, “services(1) Cainiao Cloud”). So, the structure of the table is completely destroyed. If this chunk is retrieved for RAG, LLM is unable to perceive any meani... | 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:}, {... |
OCR for text positioning and recognition;
2. Physical document object detection;
3. Cross-column and cross-page trimming;
4. Reading order determination;
5. Table structure recognition;
6. Document logical structure recognition.
Readers might refer to [2] for the details of these steps. After parsing, we use the parag... | 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 tables, it outputs the text in each table cell and also tells which cells are merged into a new one. Moreover, for documents with hierarchical headings, it outputs the hierarchical structure of the document. In summary, the parsed result is like a well-organized Word file. Figure 5 shows a scan-copy page and its pa... | 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:}, {... |
1. As shown in the “3 Visualization” part, it recognizes the mixed layout and correctly sets the whole table as a separate chunk. For paragraphs, as shown in chunk 2 in the “2 Chunking Result” part, text lines in the same paragraphs are merged together, making it easier to understand.
2. In the “2 Chunking Result” part... | 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:}, {... |
# 8030
|JSON|HTML|
|---|---|
|payu|elerent_Typo|
|Fort TABLE Contractors' Background Information|continued: Talso|
|styles|Fonts Z0|
|margin_top|margin_bottom|
|index|Fpago|
|elementtype|continued-false|
|cols: 10|Fioxt|
|0_1: text: Years in business|0_2: Jic Number Of employees|
|0_37Ftoxt: Construction typo -| |
Fi... | 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:}, {... |
Zoom in to see the details.
# Experiments on the Impact of PDF Recognition on RAG
Back to the main topic of this paper, does the way a document is parsed and chunked affect the quality of answers provided by an RAG system? To answer this, we have carried out a systematic experiment to assess the impacts.
# Quantitat... | 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:}, {... |
# Case 1: ChatDOC PDF Parser
|1 Original Page:|2 Chunking Result:|
|---|---|
|[Chunk 1] <Page Header> Management Discussion and Analysis | | Year ended March 31, 2021 | Year ended March 31, 2021 | Year ended March 31, 2021 | Year ended March 31, 2021 | Year ended March 31, 2021 | Year ended March 31, 2021 | Year ended... | 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:}, {... |
Comparative figures were reclassified to conform to this presentation. (2) Unallocated expenses primarily relate to corporate administrative costs and other miscellaneous items that are not allocated to individual segments. The goodwill impairment, and the equity-settled donation expense related to the allotment of sha... | 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:}, {... |
One of the most ideal cases is that a ToC is already prepared, made by the author(s) of the document. And yet, even in the absence of a pre-determined ToC, it can be arbitrarily generated, for example, using a generative model or in a manual way, based on the document's quantitative, semantic, or individual divisions. ... | 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:}, {... |
# Steps
|PDF Parsing|ChatDOC (PDFlux-LLM)|Baseline (PyPDF-LLM)|
|---|---|---|
| |PDFlux (Deep Learning-based)|PyPDF (Rule-based, default method in LangChain)|
|Chunking|≈300 tokens per chunk|≈300 tokens per chunk + separator|
| |+ chunking via paragraphs, tables etc.| |
|Embedding| |text-embedding-ada-002|
|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:}, {... |
# Extractive Questions
|Number|86|216|
|---|---|---|
|Question Examples| | |
| |1. Locate the content of section ten, what is the merged operating cost in the income statement?|1. Summarize and analyze the profit forecast and valuation in the research report.|
| |2. What is the specific content of table 1.|2. Fully 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:}, {... |
# Table 3. The comparison result between ChatDOC and Baseline.
| |Total|ChatDOC wins|Tie|Baseline wins|
|---|---|---|---|---|
|Extractive Questions|86|42 (49%)|36 (42%)|8 (9%)|
|Comprehensive Questions|216|101 (47%)|79 (37%)|36 (17%)|
|Summary|302|143 (47%)|115 (38%)|44 (15%)|
# Figure 7. Distribution of rating score... | 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:}, {... |
Distribution of rating scores of comprehensive analysis questions.
|Score of Baseline|0-5|5-6|6-7|7-7.5|7.5-8|8-8.5|8.5-9|9-9.5|9.5-10|
|---|---|---|---|---|---|---|---|---|---|
|0-5|8|2|0|0|1|0|1|2|1|
|5-6|3|3|1|0|0|2|3|0|0|
|6-7|1|2|4|2|0|1|1|1|0|
|7-7.5|0|0|3|0|0|3|3|2|0|
|7.5-8|1|1|5|1|5|7|4|0|0|
|8-8.5|0|2|4|4|4|... | 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:}, {... |
# Case A: ChatDOC
Query: How much cargo can I carry at most in terms of size?
| 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:}, {... |
|Cargo Volume| |
|---|---|
|Front Trunk|3.1 cu ft (88 L)|
|Behind 2nd row|19.8 cu ft (561 L)|
|Maximum total cargo volume with 5 passengers|22.9 cu ft (649 L)|
Based on the document, the maximum cargo capacity in terms of size for the Model 3 is as follows:
- Front Trunk: 3.1 cu ft (88 L)
- Behind 2nd row: 19.8 cu ft... | 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:}, {... |
# Case B: ChatDOC
Query: What is the dependent variable in table 8?
Retrieved Text Chunks:
Answer:
[Chunk 1]
TABLE 8 ESTIMATES OF RETURNS TO COGNITIVE ABILITY, NONCOGNITIVE ABILITY, AND FACTOR LOADINGS OF UNOBSERVED HETEROGENEITY IN HIGH SCHOOL AND COLLEGE
The dependent variable in Table 8 is the estimate of retu... | 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:}, {... |
error|Estimate|Std. error|
|High school|1968|0.038|0.021|0.064|0.024|0.030|
| |2000|0.047|0.025|0.091|0.028|0.225|0.023|
|College|1968|0.085|0.038|0.082|0.045|-0.129|0.054|
| |2000|0.151|0.039|0.100|0.037|0.079|0.037|
Original Page Containing the Retrieved Chunks (Correct):
Figure 11. ChatDOC’s result on locating a 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:}, {... |
Case B: Baseline (PyPDF + LangChain)
|Query: What is the dependent variable in table 8?|Retrieved Text Chunk:|Answer:|
|---|---|---|
|[Chunk 1]|The estimates and standard errors can be obtained upon request. coefficients on a quartic in periods, where a period is defined to be calendar year minus 1968. From this figur... | 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:}, {... |
Both the title and the content of the table were necessary for identifying these variables. Figure 11 and Figure 12 show how ChatDOC and Baseline perform in this case.
ChatDOC effectively retrieves the entire table, encompassing both its title and content. This comprehensive retrieval allows for an accurate response t... | 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) Heading selection
A prompt, which contains both a query and a ToC, is passed to an LLM-based generative model and the model is asked to autonomously select the headings most pertinent to the query or those that help the most to find information concerning the query. Multiple heading selections can be performed us... | 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:}, {... |
# Figure 13. An example of ChatDOC encountered the ranking and token limit issues.
|ChatDOC|Query: how many occupation dummies are included in the regression in table 4?|
|---|---|
|Retrieved Text Chunks:|Answer:|
|[Chunk 1] Table 4: Education, Occupational Choice, and the Height Premium|In Table 4, the regression inc... | 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:}, {... |
⁎⁎⁎ p b 0.01.| |
# Figure 14. An example that ChatDOC fails to retrieve the relevant table (original document: [8]).
- Baseline does not retrieve true “Table 8”, but only a text chunk below “Table 7” (since it contains the text of “Table 8). Due to the baseline’s segmentation strategy, the content of “Table 8” and ot... | 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:}, {... |
# Discussion on Limitations
While ChatDOC generally performs well, there are instances where its retrieval quality is not as good as Baseline’s. We observe two patterns in these cases.
Ranking and Token Limit Issue. If ChatDOC retrieves a large, but irrelevant table first, it uses up the context window, preventing 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:}, {... |
Alibaba Group Holding Limited. Fiscal year annual report 2023. https://static.alibabagroup.com/reports/fy2023/ar/ebook/en/index.html, 2023.
2. | 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:}, {... |
Rongyu Cao, Hongwei Li, Ganbin Zhou, and Ping Luo. Towards document panoptic segmentation with pinpoint accuracy: Method and evaluation. In 16th International Conference on Document Analysis and Recognition, pages 3–18, 2021. | 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] ChatDOC Team. https://pdfparser.io/
[4] Daisho Microline Holdings Limited. Fiscal year annual report 2022. https://www1.hkexnews.hk/listedco/listconews/sehk/2022/0626/2022062600094.pdf, 2022.
[5] Peiyi Wang, Lei Li, Liang Chen, Dawei Zhu, Binghuai Lin, Yunbo Cao, Qi Liu, Tianyu Liu, and Zhifang Sui. Large language ... | 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:}, {... |
Returns to skills and pe college premium. Journal of Money, Credit and Banking, 43:39–86, 2011. https://sci-hub.hkvisa.net/https://doi.org/10.1111/j.1538-4616.2011.00410.x.
[8] Tom S. Vogl. Height, skills, and labor market outcomes in mexico. NBER Working Paper Series, 2012. https://www.nber.org/system/files/working_pa... | 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:}, {... |
A More Cases on PDF Parsing & Chunking
Case 2 in Figure 15 features a large borderless table that spans two pages. Figure 15 shows the result by PyPDF. A close inspection reveals that tables are represented merely as sequences of text, making them challenging to interpret and understand. And the table is scattered... | 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 this case, the table that spans two pages is set into one chunk, with its title at the beginning. So, the information in this chunk is self-contained. If this chunk is retrieved for RAG, the LLM can digest useful information within it. | 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:}, {... |
# Case 2: PyPDF
|Original Pages:|Chunking Result:|
|---|---|
|1|Visualization of Chunking Result:|
|2| |
[Chunk 1]
1
Hong Kong Exchanges and Clearing Limited and The Stock Exchange of Hong Kong
Limited announcement, make no takeno responsibility for pe contents of pis
representation as to its accuracy or completeness... | 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 response, the model consults the augmentations to generate a response to the 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:}, {... |
Zoom in to see the details. | 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:}, {... |
# Case 2: ChatDOC PDF Parser
Original Pages:
2
Chunking Result:
[Chunk 1]
<td | 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
1) Comparative exploration of LLM-based vector embeddings in the KM and CM domains.
This experiment aimed to identify and exemplify the relative representational defects of LLM-based vector embedding in niche domains compared to other well-established domains. To explain this point, we conducted a compa... | 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 the other hand, ‘Physiology'(23) was chosen for the CM domain. To investigate the impact of language on representational differences in embeddings, we collected documents with the exactly identical contents from both the English version and the Korean-translated version of ‘Physiology'. The titles of the selected do... | 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:}, {... |
documents. The human-evaluated document relatedness scores were then obtained by averaging the two doctors' scores in KM and CM documents, respectively.
The correlation analyses were conducted between human-evaluated document relatedness scores and embedding correlation coefficients, and between embedding correlation ... | 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:}, {... |
5. | 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:}, {... |
---'.
Don't say anyping oper pan pe format.
If pe question is about greetings or casual talks, just say 'Disregard pe reference.'.”
aThese represent pe placeholders for conversational buffer memory, pe user’s query, and pe table of | 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:}, {... |
Broadly speaking, RAG- | 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 2. The prompts for answer generation
Prompt 1: Answer generation wip selected headings
"You are a chatbot based on a book called '현대한의학개론'.
Here is a record of previous conversation for your smoop chats.:
{history}a
Reference:
{context}a
Question: {question}a
Use pe reference to answer pe question.
The reference ... | 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:}, {... |
Be informative, gentle, and formal.
If you can't answer pe question wip pe reference, just say like 'I couldn't find pe right answer pis | 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:}, {... |
# time
Answer in Korean:”
# Prompt 2: Answer generation without selected headings for casual queries
“You are a chatbot based on a book called '현대한의학개론'. Here is a record of previous conversation for your smooth chats.: {history}a
Question: {question}a
Answer the 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:}, {... |
Be informative, gentle, and formal. Answer in Korean:”
These denote the placeholders for conversational buffer memory, the reference based on the selected heading, and the user’s query, respectively, from top to bottom.
Conversation buffer memory was incorporated in the prompts for both heading selection and answer g... | 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:}, {... |
# Tasks and performance evaluation metrics
To evaluate the performance of our domain-specific, prompt-RAG-based chatbot and the other baseline models, we composed a series of 30 questions related to KM. The models were to generate answers to those questions in order.
| 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:}, {... |
Each question was categorized into one of the three types to examine the models’ capabilities in direct retrieval, comprehensive understanding, and functional robustness. The questions among the three types followed a ratio of 4:4:2. For the ChatGPT baselines, which do not utilize retrieval augmentation, questions spec... | 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:}, {... |
# Statistical analysis
To evaluate the statistical significance of our model’s scores in relation to those of the others, we performed t-tests and Mann-Whitney U tests. The t-tests compared the scores across the criteria of relevance, readability, and informativeness, while Mann-Whitney U tests were applied to the sco... | 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:}, {... |
All statistical analyses were conducted with the Statsmodels(36) package in Python 3.11. | 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:}, {... |
# Results
1) Comparative analysis of LLM-based vector embeddings in KM and CM
(1) Comparison of KM and CM document pairs by correlation metrics
Human-evaluated document relatedness scores, embedding correlation coefficients, and token overlap coefficients were calculated for KM and CM document pairs using three diff... | 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) Correlation analyses between metrics in KM and CM documents
12 | 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:}, {... |
# Num. of Evidence Needed
|Count|Percentage|
|---|---|
|0 (Null Query)|301|11.78%|
|2|1078|42.18%|
|3|779|30.48%|
|4|398|15.56%|
|Total|2,556|100.00%|
Table 4: The distribution of the number of evidence required to answer multi-hop queries in MultiHop-RAG.
Related tasks can be categorized as retrieval-related tasks ... | 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 analyze the correlations between human-evaluated document relatedness scores and embedding correlation coefficients, and between embedding correlation coefficients and token overlap coefficients, Pearson or Spearman correlation coefficients were calculated for each metric pair. Figure 3 provides scatter plots for sh... | 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:}, {... |
Across all evaluated | 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:}, {... |
# models
E5-mistral-7b-instruct, voyage-02, and text-embedding-ada-002—the correlation coefficients for CM were consistently higher than those for KM, indicating a stronger alignment with human judgment in the context of CM. Within CM, the coefficients for CM_EN were higher than those for CM_KR. Specifically, 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:}, {... |
|Embedding model|Embedding correlation coefficient (Spearman's ρ)|Token overlap coefficient (Pearson's r)|
|---|---|---|
|KM|CM_KR| |CM_EN|KM|CM_KR|CM_EN|
|E5-mistral-7b-instruct|0.503b|0.691c|0.725c|0.304|0.365|0.438a|
|voyage-02|-0.016|0.376|0.670c|0.429a|0.177|0.518b|
|text-embedding-ada-002|0.167|0.563c|0.625c|0.50... | 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:}, {... |
# Abbreviations:
KM, Korean medicine; CM, CM_KR, CM physiology in Korean; CM_EN, CM physiology in English.
Overall, embedding correlations in CM_EN consistently demonstrates a higher alignment with human-evaluated document relatedness compared to KM and CM_KR. On the contrary, the embedding representation of KM tends... | 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 readability scores of our model were significantly higher compared to C100-V150, and especially for informativeness, our model obtained statistically significant scores, approximately 15. | 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:}, {... |
2.5 times that of C50-V300 and around 1.9 times that of C100-V150. However, our mode was significantly slower in terms of average response time, taking an additional 18.356 seconds compared to C50-V300 and 17.806 seconds more than C100-V150. These results find that the Prompt-RAG model excelled in answer quality, while... | 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:}, {... |
(A) Direct retrieval questions. (B) Comprehensive understanding questions. (C) Functional robustness questions. The asterisks | 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:}, {... |
Our model reached an average score of 5.5 for direct retrieval, 5.389 for comprehensive understanding, and 5.444 for functional robustness out of 6, outdoing all other models in every question type. Notably, the scores for direct retrieval were significantly higher compared to those of all the other models, and the sco... | 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 suggests not only our model's advanced capability for retrieval but also its comprehension-based answering performance, which is comparable to ChatGPT-4. | 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:}, {... |
A retrieval-related task focuses on retrieving relevant text from the knowledge base, while a generation-related task focuses on generating high-quality responses given the retrieved text. In this section, we showcase two use cases for each task where MultiHop-RAG can be employed.
# 4.1 Retrieval-related Task
An impo... | 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:}, {... |
# Discussion
In this study, our exploration of LLM-based vector embeddings revealed marked limitations within the KM domain. The analysis showed that vector embeddings are heavily influenced by languages and token overlaps, which are not always compatible with human reasoning, potentially leading to suboptimal perform... | 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 applicability and efficiency can expand vastly, together with natural language processing techniques developing and improving. As the cognitive abilities of LLMs continue to advance, we look forward to Prompt-RAG becoming an even more powerful tool with full reliance on the capabilities of an LLM itself.
Its wide-... | 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 rapid advancements | 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 generative models suggest that the limitations of our model will become increasingly less problematic in the foreseeable future, likely sooner than anticipated.
# 19 | 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:}, {... |
# Conclusion
We suggest Prompt-RAG as an alternative to the conventional vector embedding RAG methods, addressing the limitations of LLM-based vector embeddings in niche domains where inconsistencies with human reasoning can lead to suboptimal performance. With its derived QA chatbot, Prompt-RAG has achieved notable o... | 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:}, {... |
Providing a new paradigm in RAG, it contributes to the advancement of information retrieval in specific domains with remarkable ease. | 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:}, {... |
# Reference
|1.|Lewis P, Perez E, Piktus A, Petroni F, Karpukhin V, Goyal N, et al. Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in Neural Information Processing Systems. 2020;33:9459-74.|
|---|---|
|2.|Shuster K, Poff S, Chen M, Kiela D, Weston J. Retrieval augmentation reduces hallucina... | 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;5(3):220-35.|
|16.|Cha W-S, Oh J-H, Park H-J, Ahn S-W, Hong S-Y, Kim N-I. Historical difference between traditional Korean medicine and traditional Chinese medicine. Neurological Research. 2007;29(sup1):5-9.|
|17.|Yin CS, Ko S-G. Introduction to the History and Current Status of Evidence-Based Korean Medicine: A U... | 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:}, {... |
2023:2023.10.26.23297629.|
|21.|Jang D, Yun T-R, Lee C-Y, Kwon Y-K, Kim C-E. GPT-4 can pass the Korean National Licensing Examination for Korean Medicine Doctors. PLOS Digital Health. | 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;2(12):e0000416.| | 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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