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# arXiv:2401.15391v1 [cs.CL] 27 Jan 2024
MultiHop-RAG: Benchmarking Retrieval-Augmented Generation for Multi-Hop Queries
Yixuan Tang and Yi Yang
Hong Kong University of Science and Technology
{yixuantang,imyiyang}@ust.hk
Abstract
Retrieval-augmented generation (RAG) augments large language models (LLM) by retriev... | 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 financial analyst might query, "Which company among Google, Apple, and Nvidia reported the largest profit margins in their third-quarter reports for 2023?" or inquire about a specific company’s performance over time, such as "How does Apple’s sales trend look over the past three years?" These queries require evidence... | 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 4: Query and Answer Generation. In this step, we leverage the bridge-entity or bridge-topic to generate multi-hop queries. Specifically, we first group the claims having the same bridge-entity or | 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:}, {... |
Published as a Tiny Paper at ICLR 2024
# APPENDIX A
The prompts used for the LLM in our experiments are as follows:
- System Prompt: Answer the questions based on the paragraphs provided here. DO NOT use any other information except that in the paragraphs. | 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 2nd FutureDial challenge focuses on building dialog systems with RAG, with the following features:
- We release a new dataset from the China Mobile customer-service logs (MobileCS2) that contains both labeled and unlabeled data, which encourages the study of semi-supervised RAG-based dialog systems. | 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:}, {... |
# Version 1.0 (April 29, 2024)
The dataset enables the study of building dialog systems with knowledge base queries and API calls. The dataset is available in both Chinese and English versions to the public, so that researchers around the world can experiment with this dataset.
To enable a RAG-based dialog system 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:}, {... |
Offline corpus-based evaluation will be conducted to test the performance of the submitted system.
# THE MOBILECS2 DATASET
The MobileCS2 dataset is derived from the China Mobile real-world conversational scenarios and comprises around 6,000 processed dialog logs (nearly 3,000 carefully annotated) between customers 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:}, {... |
# ANNOTATION DETAILS
In the customer service scenario, there are some knowledge or information that the customer service agent needs to get from knowledge bases (KBs) in order to correctly respond to the user. Therefore, to annotate the necessary knowledge or information, the annotators should imagine themselves as cu... | 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:}, {... |
# Version 1.0 (April 29, 2024)
**Table 1: Detailed description for Api query annotation. The Chinese version can be seen in Appendix.**
|Main class|Api query|Description|
|---|---|---|
|QA|[QA]|Consult the FAQ manual, which includes a collection of commonly asked questions such as recent promotional packages and gener... | 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:}, {... |
Turns labeled as “Search for user information” can be consolidated into a user database (local kb) within a single dialog. Meanwhile, turns labeled as “search for products information” can be aggregated into a product database (global kb) across the entire dataset. These three databases largely emulate the channels thr... | 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:}, {... |
# Version 1.0 (April 29, 2024)
'log":
'systemManual" _
user" 'The seasons are changing; but our deep affection remains the same: Meeting you is the most beautiful moment: If there is anything can assist you with, please feel free to tell me:
'api_query"
'api_result"=
user" "Is my current package discounted? custo... | 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:}, {... |
(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:}, {... |
# Version 1.0 (April 29, 2024)
To train the dialog system pθ (rt | ct, ht), we use the standard auto-regressive loss to optimize the generation probability initialized :
pθ (rt | ct, h) = Π |pθ (yl | ct, h, y1, . . | 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:}, {... |
. , yl−1)|rt
where | · | denotes the length in tokens, and yl the l-th token of rt and pθ is initialized with a GPT-based pretrained language model (Radford et al., 2019).
# 5.2 METRICS AND EVALUATION
Given a dialog X and its knowledge base KBX , the retrieval system needs to rank the relevance score for each knowle... | 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:}, {... |
Keep the answers as short as possible. JUST GIVE THE ANSWER. NO PREAMBLE REQUIRED.
- User Prompt: “PARAGRAPHS : ”+context + “QUESTIONS: ” + query
# APPENDIX B
| |0-50|50-100|100-150|150->00|
|---|---|---|---|---|
|0-50|50-100|50-100|50-100|50-100 vs 150-200|
|50-100|50-100|50-100|50-100|50-100 vs 150-200|
|100-150|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:}, {... |
# SUBMISSION GUIDELINES
Each team needs to submit a package via email to FutureDialRAG@gmail.com before the Entry Submission Deadline. The package should contain a clear README documentation for running the system over the evaluation data. | 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 submitted system should be in one of the following two forms. In either form, the system’s processing speed should be no less than 10 tokens per second.
- The submission package contains the system executable with the model, for example, in a Docker image. All dependencies are contained in the submission package. ... | 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:}, {... |
# Version 1.0 (April 29, 2024)
Michael Glass, Gaetano Rossiello, Md Faisal Mahbub Chowdhury, Ankita Naik, Pengshan Cai, and Alfio Gliozzo.
Re2g: Retrieve, rerank, generate.
In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies,... | 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:}, {... |
2701–2715, 2022.
Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Ming-Wei Chang.
Realm: retrieval-augmented language model pre-training.
In Proceedings of the 37th International Conference on Machine Learning, pp. 3929–3938, 2020.
Samuel Humeau, Kurt Shuster, Marie-Anne Lachaux, and Jason Weston.
Poly-encode... | 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–2208, 2022b.
| 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:}, {... |
Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.
Dense passage retrieval for open-domain question answering.
In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 6769–6781, 2020.
Patrick Lewis, Ethan Pere... | 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:}, {... |
# Version 1.0 (April 29, 2024)
|Timo Schick, Jane Dwivedi-Yu, Roberto Dessi, Roberta Raileanu, Maria Lomeli, Eric Hambro, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom.|Toolformer: Language models can teach themselves to use tools. In Thirty-seventh Conference on Neural Information Processing Systems, 2023.|
|... | 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 Empirical Methods in Natural Language Processing (EMNLP), 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:}, {... |
# APPENDIX
|主类|Api query|解释|
|---|---|---|
|QA类|[QA]|查询FAQ手册 包含一些常用问题 如最近优惠的套餐、普遍的业务规则等。|
|置空类|-|根据上下文信息 客服人员无需进行额外的查询便能顺利的完成对话。|
|查询特定业务信息|查询移动当前有的业务信息|如特定的套餐、流量包等。|
|API-查询类|查询用户已办理的业务|查询用户当前已经拥有的业务 包括当前套餐、当前月租、当前流量等。|
|查询其他信息|例如查询流量短信|查询其他用于完成对话的关键信息。比如查询历史轨迹中移动10086给用户发送的超出流量提醒短信、查询营业厅地址等|
|API-取消类|取消|取消用户当前拥有的某个业... | 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:}, {... |
# DomainRAG: A Chinese Benchmark for Evaluating Domain-specific Retrieval-Augmented Generation
Shuting WangPeidong Guo2, Kun Fang1, Shiren Song, and Zhicheng Dou1∗1, Jiongnan Liu2, Yutao Zhu11, Jiehan Cheng1, Yuqi Fu1
1Gaoling School of Artificial Intelligence, Renmin University of China2Baichuan Intelligent Technolo... | 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:}, {... |
|ID|Query|Expected answer|Use full sentence|Use defined word|Use definition|Observations|
|---|---|---|---|---|---|---|
|1.|Explain EIRP|effective isotropic radiated power (EIRP): The equivalent power of a transmitted signal in terms of an isotropic (omnidirectional) radiator.|Effective isotropic radiated power (EIRP):... | 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 example, consulting firm financial statements or data aggregation in the investment industry are all widely used scenarios of RAG systems. Nevertheless, due to the problem of data privacy, these corpora cannot be incorporated into the training data of LLM, hence RAG systems are needed to plug these data into the LL... | 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:}, {... |
# Understanding of User Intents
In traditional web information retrieval methods, such as search engines, understanding the actual user intents has always been a crucial step and studied in the literature (Zhou et al., 2020; Yao et al., 2020; Wang et al., 2023a,b; Zhu et al., 2021; Chen et al., 2022; Wang et al., 2024... | 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:}, {... |
It is also important for LLMs to comprehend the structural information from the provided knowledge, hence providing accurate and reliable responses. Furthermore, the inherent difficulty for LLMs in acquiring in-domain knowledge underscores the importance of trusting external expert knowledge to bridge gaps in their per... | 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 addition to an extractive QA dataset that assesses basic QA ability, we further annotated the following sub-datasets, each targeting a specific ability, i.e., conversational QA, structural QA, faithful QA, time-sensitive QA, noisy QA, and multi-document QA. Concretely, the conversational QA dataset simulates complex... | 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:}, {... |
# What was the predecessor of the School of Arts?
The predecessor of the College of Literature and Art was...
# Which year was it founded?
1939
# References
|Structural references|Ability to analyze structural information|Anti-Reference|
|---|---|---|
|669|60| |
# What majors does the School of Philosophy offer?
... | 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:}, {... |
80
# What are the differences between Computer Science and AI?
| 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:}, {... |
Multiple references related to CS and AI.
The differences are...
# Who was the president in 2019?
References
The president in 2019 is...
# Figure 1: Important abilities for RAG models.
Formation is not yet well-developed in all LLMs. Therefore, when deploying RAG models in practice, it is crucial to choose an LLM... | 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:}, {... |
ability under in-domain situations. To thoroughly assess the abilities of RAG models, in this paper, we propose to leverage an in-domain document corpus collected from the enrollment websites of a Chinese university to evaluate the capabilities of RAG from multi-aspects.
# 3 Evaluate Retrieval-Augmented Generation via... | 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, they need to heavily depend on external knowledge resources. To comprehensively evaluate the aforementioned capabilities of RAG models, we annotated seven sub-datasets, and the corresponding data construction process is demonstrated below.
# 3.1 Data Construction
To acquire the document corpus for this scena... | 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:}, {... |
Furthermore, to ensure the generation quality, we manually filtered out the QA pairs that could be answered using knowledge contained within LLMs themselves. Finally, we modify the answer-related information in the positive references to build anti-references and corresponding anti-answers.| | | 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 definition does not return it in top-3. The full definition returns it in 3rd position. | 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: Overall results on the extractive, conversational, time-sensitive, and multi-doc datasets
|Dataset|Count|Avg. Q Len|Avg. A Len|
|---|---|---|---|
|Extractive|90|25.09|8.17|
|Conversational|49|16.65|35.66|
|Structural|94|35.48|6.07|
|Time-sensitive|65|21.38|4.67|
|Multi-document|48|35.90|86.69|
|Faithfulness... | 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:}, {... |
# Experiment
# Main settings
We first conducted experiments using the following external knowledge settings,
1. Close-book: No external domain-specific knowledge was provided to assess whether LLMs could solve these expert problems themselves.
| 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. Golden reference: We provided human-annotated positive references for LLMs to explore the upper bounds of their abilities.
3. Retrieved reference: Simulating real-world applications of RAG models, we provided them with retrieved documents. We chose BM25 (Robertson and Zaragoza, 2009) and BGE-base-zh-v1.5 (Xiao et al... | 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:}, {... |
# 1.0 Pure Text
|answers are contained by predictions (EM), the|HTML|
|---|---|
|one assesses whether the predictions are strictly|HTML|
|the same as the answers (EMS); F1 is used to eval-|0.8|
|uate models in the perspective of term-matching;|0.6|
|Rouge-L and GPT-4 evaluation (GE) are used to|0.4|
|assess the perfor... | 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:}, {... |
Therefore, we proactively filtered out the information irrelevant to the valuable content of web pages. Nevertheless, the processed contents still exceed the maximum length of some LLMs, e.g., Llama. For simplicity, we directly truncated the provided information for LLMs that cannot handle lengthy texts. We expect that... | 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: Overall results on the extractive, conversational, time-sensitive, and multi-doc datasets
|Settings|Models|Extractive| | | | |Conversational| | | | |Time-sensitive| | | |Multi-doc|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| | | | | | | | | | | | |EM|EMS|F1|Rouge-L|Rouge-L|GE|EM|... | 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:}, {... |
# 4.5 Robustness of LLMs on Noisy References
To assess the robustness of LLMs on noised references, we mixed the positive references with different amounts of noisy references, including 4, 9, 14, 19, and 24. Additionally, the position of the positive reference was varied, i.e. the first, the middle, and the last posi... | 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:}, {... |
# phenomenon. Placing positive references in the
| |0.9|0.80|
|---|---|---|
| |0.8|0.75|
| | |NC=4|NC=19|
|NC=9|NC=24|0.70|
|0.7|NC=14|0.65|
phenomenon has also been indicated in recent studies (Liu et al., 2024b), highlighting the importance of not only the quality of the provided knowledge but also its order.
| |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:}, {... |
The results of “No Noise” are not always the best compared to those obtained from noisy references.
The reason may be one document is provided, the noisy references contain NC + 1 external documents.
The increased amount of provided knowledge may emphasize the confidence of LLMs in external knowledge, making them mor... | 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:}, {... |
Limitations
In this work, we identified six critical capabilities of RAG models and developed a comprehensive dataset, namely DomainRAG, to evaluate these capabilities in a domain-specific application scenario. We acknowledge the following limitations of our current study that present opportunities for future investig... | 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, top similarity in definition > correct answer in defined word. Similarly, wrong 2 answers in the full definition have higher similarity than correct answer in full sentence.|
| | |beamforming: A spatial filtering mechanism used at a transmitter to improve the received signal power or signal-to-noise ratio (SNR... | 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:}, {... |
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:}, {... |
2024. Benchmarking large language models in retrieval-augmented generation. In Thirty-Eighp AAAI Conference on Artificial Intelligence, AAAI 2024, Thirty-Sixp Conference on Innovative Applications of Artificial Intelligence, IAAI 2024, Fourteenp Symposium on Educational Advances in Artificial Intelligence, EAAI 2014, 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:}, {... |
2023. Contrastive learning for user sequence representation in personalized product search. In Proceedings of pe 29p ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD ’23, page 380–389.
Bhuwan Dhingra, Jeremy R. Cole, Julian Martin Eisenschlos, Daniel Gillick, Jacob Eisenstein, and William W. Cohen. 202... | 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:}, {... |
Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Ming-Wei Chang. 2020. REALM: retrieval-augmented language model pre-training. CoRR, abs/2002.08909.
Zhengbao Jiang, Frank F. Xu, Luyu Gao, Zhiqing Sun, Qian Liu, Jane Dwivedi-Yu, Yiming Yang, Jamie Callan, and Graham Neubig. 2023. Active retrieval augmented gener... | 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:}, {... |
# more with less: Understanding prompt learning behaviors through gist compression
Preprint, arXiv:2402.16058.
# Xi Victoria Lin, Xilun Chen, Mingda Chen, Wei-jia Shi, Maria Lomeli, Rich James, Pedro Rodriguez, Jacob Kahn, Gergely Szilvasy, Mike Lewis, Luke Zettlemoyer, and Scott Yih. 2023. RA-DIT: retrieval-augmente... | 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:}, {... |
# Jiongnan Liu, Zhicheng Dou, Jian-Yun Nie, and Ji-Rong Wen. 2024a. Integrated personalized and diversified search based on search logs. IEEE Transactions on Knowledge and Data Engineering, 36(2):694–707.
# Jiongnan Liu, Zhicheng Dou, Qiannan Zhu, and Ji-Rong Wen. 2022. A category-aware multi-interest model for person... | 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:}, {... |
# Nelson F. Liu, Kevin Lin, John Hewitt, Ashwin Paran-jape, Michele Bevilacqua, Fabio Petroni, and Percy Liang. 2024b. Lost in the Middle: How Language Models Use Long Contexts. Transactions of the Association for Computational Linguistics, 12:157–173.
# Fabio Petroni, Aleksandra Piktus, Angela Fan, Patrick Lewis, Maj... | 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:}, {... |
# Stephen Robertson and Hugo Zaragoza. 2009. The probabilistic relevance framework: Bm25 and beyond. Found. | 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:}, {... |
Trends Inf. Retr., 3(4).
# Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, and Ashish Sabharwal. 2023. Interleaving retrieval with chain-of-thought reasoning for knowledge-intensive multi-step questions. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long 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:}, {... |
Syst., 42(3).
# Shuting Wang, Zhicheng Dou, Jing Yao, Yujia Zhou, and Ji-Rong Wen. 2023a. Incorporating explicit subtopics in personalized search. In Proceedings of the ACM Web Conference 2023, WWW ’23, page 3364–3374, New York, NY, USA. Association for Computing Machinery.
# Shuting Wang, Zhicheng Dou, and Yutao Zhu... | 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:}, {... |
beamforming steering matrix: A matrix directed frame: See individually addressed. unknown_definition_18:NOTE These uses determined using knowledge of the (0.309) include calculation of transmit steering, calculation of recommended modulation and coding scheme (MCS), and calculation of calibration parameters. (0.359)
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:}, {... |
# Yutao Zhu, Jian-Yun Nie, Zhicheng Dou, Zhengyi Ma, Xinyu Zhang, Pan Du, Xiaochen Zuo, and Hao Jiang
2021. Contrastive learning of user behavior sequence for context-aware document ranking. In CIKM ’21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, QLD, Australia, Novem... | 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:}, {... |
# Yutao Zhu, Huaying Yuan, Shuting Wang, Jiongnan Liu, Wenhan Liu, Chenlong Deng, Zhicheng Dou, and Ji-Rong Wen
2023. Large language models for information retrieval: A survey. CoRR, abs/2308.07107. | 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:}, {... |
# Unveil the Duality of Retrieval-Augmented Generation: Theoretical Analysis and Practical Solution
Shicheng Xu1,2, Liang Pang1∗, Huawei Shen1,2, Xueqi Cheng1,2∗
1CAS Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences
2University of Chinese Academy of Sciences
{xushicheng21s... | 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], Asai et al. [2023], Ram et al. [2023], which is actually the knowledge fusion between parameters and retrieved texts. However, studies show that this fusion is not consistently effective and can even mislead LLMs due to noisy or incorrect retrieved texts Xu et al. [2023], Ram et al. [2023], Xu et al. [2024a,b],... | 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:}, {... |
Under review. | 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)Our Theoretical Results:
|Benefit|Detriment|
|---|---|
|Retrieved distribution|LLMs’ distribution|
RAG Representation
Actual effect of RAG can be predicted at token-level
Effect = Benefit − Detriment
Benefit > Detriment
Sim() is the similarity between representations
Effect is positively correlated with Sim... | 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:}, {... |
[2021], Wang et al. [2024], we propose to analyze RAG by Latent Variable Model, in which LLMs firstly infer the latent variable and then generate the texts conditioned on the latent variable. In this way, we decouple and formalize the benefit and detriment from RAG prediction as two terms in subtraction. Further deriva... | 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:}, {... |
Consequently, it brings both benefit and detriment. (2) We prove that the actual effect of RAG, which is the trade-off between benefit and detriment, can be predicted at token level (right side in Figure 1 (a)). Specifically, we find benefit and detriment bound the similarity between RAG representation and retrieved 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:}, {... |
# Understand the duality of RAG: benefit and detriment
RAG has the duality, although the retrieved texts can provide LLMs with external knowledge (benefit), it also contains the risk of misleading LLMs due to the noise in retrieved texts (detriment). This section aims to theoretically unveil this duality (i.e., benefi... | 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], Wang et al. [2024], we first propose to formalize RAG as the latent variable model. Specifically, given the token sequence x1:i−1 = {x1, x2, ...xi−1} generated from time step 1 to i − 1, from the perspective of the latent variable model, the probability distribution of the token xi at the i-th step can be descr... | 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:}, {... |
(0.418) Unable to identify this despite it being available as a keyword in the actual definition | 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]. Inspired by this, we analyse RAG as sampling the Retrieved Concept z∗ from the input retrieved texts list R = {r1, r2, ..., rn} (ri is a retrieved passage), and then predicting p(xi|R, x1:i−1), which can be formalized as:
p(xi|R, x1:i−1) = ∫∫ p(xi|R, x1:i−1, z)p(z|R, x1:i−1) dz
= ∫−{z∗}p(xi|R, x1:i−1, z)p(z|R,... | 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:}, {... |
# Decouple and formalize benefit and detriment
Recapping the view that distribution difference brings both benefit and detriment in RAG we want to illustrate (Section 2.1), next, we derive the relationship between knowledge fusion and distribution difference from Equation 2 to decouple and formalize the benefit and de... | 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:}, {... |
Corollary 1. Two terms about distribution difference in Equation 9 explain the occurrence mechanism of benefit and detriment respectively. A larger distribution difference not only indicates more out-of-distribution knowledge (benefit) but also implies the LLMs’ resistance to the retrieved texts that contradict the pre... | 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 prediction of RAG
and find that both benefit and detriment bound the similarity between p(xi|R, x1:i−1) and pR(xi|x1:i−1), which can serve as an important signal indicating the value order between benefit and detriment at token level. Specifically, recapping the Equation 2 that describes the knowledge 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:}, {... |
Our detailed proof of Theorem 1 can be found in Appendix D.
Theorem 2
D is the difference, so D1 can be treated as similarity between p(xi
R, x1:i−1) and pR(xi
x1:i−1). The result of benefit minus detriment is approximately positively correlated with D 1: KL(pR(r)∥p(r
z))− KL(pR(r)∥p(r
z
{z }
{z ∗))∝ D } 1. benefit de... | 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:}, {... |
Section 2.3 shows that the result of benefit minus1. So the value of D when benefit minus1 detriment is approximately positively correlated with D detriment is zero is an important dividing point. A D1 greater than this value indicates that benefit is greater than detriment, and conversely, the benefit is less than det... | 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:}, {... |
# stage1
| |0.8|
|---|---|
| |0.6|
| |0.4|
| |0.2|
| |0.0|
| |0|5|10|15|20|25|30|
Attention score
Figure 2: Attention score for xi (blue line) and difference of word distribution change (yellow line) vary with layers. stage 1: Lexical and Syntactic. | 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:}, {... |
stage 2: Text Matching. stage 3: Knowledge Fusion.
3.1 Distribution prediction for retrieved texts
Based on our theoretical analysis in section 2.2 and the detailed proof in Appendix G, we find that:
Corollary 2. RAG is unsupervised In-context Learning that fuses the distribution from retrieved texts with LLMs’ pre-... | 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], Schuster et al. [2022] prove the language word distribution of hidden states in each layer by language heads ϕ as ϕ(hil), in which hi is the heads can be directly applied to the hidden states of middle layers, so we propose to obtain the hidden states for token xi in the l-th layer. Then we can measure the word... | 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 above two results indicate that: When performing RAG, LLMs first perform text matching in the middle layers, extracting relevant knowledge from the retrieved texts. As the depth increases, the matching becomes more and more accurate, and it reaches a turning point. In the deep layers after this turning point, LLMs ... | 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:}, {... |
(EPD) destination addresses, priority, drop frame: A unit of data exchanged protocol instance: An execution of a particular unknown_definition_9: NOTE See IETF RFC 4282. (0.407)
identified? eligibility, service class, optional set between peer protocol entities. (0.432) protocol that consists of the state of the commu... | 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:}, {... |
Matching as distribution. The matching information between R = [rt1, rt2, ..., rtm] (rt is the token in R) and token xi around turning point can be used to approximate the distribution pR(xi|x1:i−1) of the retrieved texts R conditioned on x1:i−1 at the l∗-th layer. The matching information consists of two parts, one is... | 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:}, {... |
# LLMs Methods
| |# Generation|Wikitext|ASQA|Bio|NQ|
|---|---|---|---|---|---|
|Logprobs|2|65.25|64.33|68.96|67.55|65.24|64.59|55.31|51.41|
|Uncertainty|2|64.12|63.50|66.14|63.96|65.78|64.60|56.03|52.15|
|Consistency-Lexical|10|64.01|62.17|69.42|67.04|65.41|65.28|55.06|51.13|
|Consistency-Semantic|10|65.93+|66.88|64.2... | 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:}, {... |
# Experiments
# Experimental details
Experimental setup, metrics, and baselines. The core of our X-RAG is determining the value order between benefit and detriment at token level. This can be viewed as a binary classification task to determine whether benefit is greater than detriment or not. Therefore, a primary exp... | 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:}, {... |
Datasets. For the token level binary classification task in the primary experiment, we use three long-form generation tasks including long-form Q&A (ASQA Stelmakh et al. [2023]), people biographies generation (Bio Min et al. [2023]) and language modeling (Wikitext103 Merity et al. [2016]) and one short-form task includ... | 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:}, {... |
[2017] and SQuAD v1.1 Rajpurkar et al. [2016].
Implementation details. As for retrieval in RAG, we follow Xu et al. [2023] to use ColBERTv2 Santhanam et al. [2021], an excellent generalizable model as the retriever, and use Wikipedia consisting of 21,015,324 passages Karpukhin et al. [2020] as retrieval database. All ... | 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:}, {... |
# Train Add TriviaQA WebQ Squad
|Methods|LLM Module|Ratio of Hard Negative Passages|Ratio of Hard Negative Passages|Ratio of Hard Negative Passages|
|---|---|---|---|---|
| | |100%|80%|60%|40%|20%|0%|100%|80%|60%|40%|20%|0%|100%|80%|60%|40%|20%|0%|
| | |Standard RAG|no ✔|no ✔|43.8|67.0|71.3|76.2|78.2|81.9|23.9|35.8|40... | 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 results
Primary experiment. Table 1 shows that our X-RAG achieves better performance in determining the value order between benefit and detriment at token level in RAG than baselines across different tasks and LLMs. Baselines determine the value order by detecting the degree of hallucination while our X... | 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:}, {... |
LLM in this is LLaMA-2-7B.
Case study. Figure 4 in Appendix I intuitively shows the collaborative generation between pure LLM and RAG in our X-RAG in open-domain Q&A. X-RAG is effective to preserve benefit and avoid detriment at token level by dynamically selecting suitable tokens among pure LLM and RAG.
Ablation stu... | 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:}, {... |
et al. [2024a], Yoran et al. [2024]. Some methods let LLMs dynamically determine whether the query needs RAG Asai et al. [2023], Xu et al. | 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], Ren et al. [2023], Feng et al. [2023], Mallen et al. [2022], Jiang et al. | 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:}, {... |
(0.380)
unknown_definition_9: NOTE See IETF RFC 4282. (0.404)
traffic classification (TCLAS): The specification of one of several types of matching filter to classify protocol data units (PDUs) or medium access control (MAC) service data units (MSDUs) as belonging to a particular traffic stream (TS). Depending on 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:}, {... |
[2023]. All the previous works solve the contradiction between benefit and detriment in RAG from the perspective of application but lacking essential and theoretical analysis, which limits the understanding and cannot find the fundamental method to solve it. Therefore, they rely on additional modules or fine-tuning LLM... | 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:}, {... |
# Zhuoran Jin, Pengfei Cao, Hongbang Yuan, Yubo Chen, Jiexin Xu, Huaijun Li, Xiaojian Jiang, Kang Liu, and Jun Zhao. Cutting off the head ends the conflict: A mechanism for interpreting and mitigating knowledge conflicts in language models. arXiv preprint arXiv:2402.18154, 2024b.
| 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:}, {... |
# Ori Yoran, Tomer Wolfson, Ori Ram, and Jonathan Berant. Making retrieval-augmented language models robust to irrelevant context. In International Conference on Learning Representations, 2024.
# Ruiyang Ren, Yuhao Wang, Yingqi Qu, Wayne Xin Zhao, Jing Liu, Hao Tian, Hua Wu, Ji-Rong Wen, and Haifeng Wang. Investigatin... | 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:}, {... |
# Shangbin Feng, Weijia Shi, Yuyang Bai, Vidhisha Balachandran, Tianxing He, and Yulia Tsvetkov. Knowledge card: Filling llms’ knowledge gaps with plug-in specialized language models. In The Twelfth International Conference on Learning Representations, 2023.
# Alex Mallen, Akari Asai, Victor Zhong, Rajarshi Das, Danie... | 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:}, {... |
# Andrey Malinin and Mark Gales. Uncertainty estimation in autoregressive structured prediction. arXiv preprint arXiv:2002.07650, 2020.
# Zi Lin, Jeremiah Zhe Liu, and Jingbo Shang. Towards collaborative neural-symbolic graph semantic parsing via uncertainty. Findings of the Association for Computational Linguistics: ... | 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:}, {... |
# Chao Chen, Kai Liu, Ze Chen, Yi Gu, Yue Wu, Mingyuan Tao, Zhihang Fu, and Jieping Ye
Inside: Llms’ internal states retain the power of hallucination detection. arXiv preprint arXiv:2402.03744, 2024.
| 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:}, {... |
# Corby Rosset, Chenyan Xiong, Minh Phan, Xia Song, Paul N. Bennett, and Saurabh Tiwary
Knowledge-aware language model pretraining. CoRR, abs/2007.00655, 2020. URL https://arxiv.org/abs/2007.00655.
# Shi-Qi Yan, Jia-Chen Gu, Yun Zhu, and Zhen-Hua Ling
Corrective retrieval augmented generation. arXiv preprint arXiv: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:}, {... |
PMLR, 2023.
# Chi Han, Ziqi Wang, Han Zhao, and Heng Ji
In-context learning of large language models explained as kernel regression. arXiv preprint arXiv:2305.12766, 2023. | 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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