REASONS: A benchmark for REtrieval and Automated citationS Of scieNtific Sentences using Public and Proprietary LLMs

• URL: http://arxiv.org/abs/2405.02228v2
• Date: Thu, 9 May 2024 00:23:16 GMT
• Title: REASONS: A benchmark for REtrieval and Automated citationS Of scieNtific Sentences using Public and Proprietary LLMs
• Authors: Deepa Tilwani, Yash Saxena, Ali Mohammadi, Edward Raff, Amit Sheth, Srinivasan Parthasarathy, Manas Gaur,
• Abstract summary: We investigate whether large language models (LLMs) are capable of generating references based on two forms of sentence queries. From around 20K research articles, we make the following deductions on public and proprietary LLMs. Our study contributes valuable insights into the reliability of RAG for automated citation generation tasks.
• Score: 41.64918533152914
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Automatic citation generation for sentences in a document or report is paramount for intelligence analysts, cybersecurity, news agencies, and education personnel. In this research, we investigate whether large language models (LLMs) are capable of generating references based on two forms of sentence queries: (a) Direct Queries, LLMs are asked to provide author names of the given research article, and (b) Indirect Queries, LLMs are asked to provide the title of a mentioned article when given a sentence from a different article. To demonstrate where LLM stands in this task, we introduce a large dataset called REASONS comprising abstracts of the 12 most popular domains of scientific research on arXiv. From around 20K research articles, we make the following deductions on public and proprietary LLMs: (a) State-of-the-art, often called anthropomorphic GPT-4 and GPT-3.5, suffers from high pass percentage (PP) to minimize the hallucination rate (HR). When tested with Perplexity.ai (7B), they unexpectedly made more errors; (b) Augmenting relevant metadata lowered the PP and gave the lowest HR; (c) Advance retrieval-augmented generation (RAG) using Mistral demonstrates consistent and robust citation support on indirect queries and matched performance to GPT-3.5 and GPT-4. The HR across all domains and models decreased by an average of 41.93%, and the PP was reduced to 0% in most cases. In terms of generation quality, the average F1 Score and BLEU were 68.09% and 57.51%, respectively; (d) Testing with adversarial samples showed that LLMs, including the Advance RAG Mistral, struggle to understand context, but the extent of this issue was small in Mistral and GPT-4-Preview. Our study contributes valuable insights into the reliability of RAG for automated citation generation tasks.

Related papers

• CiteME: Can Language Models Accurately Cite Scientific Claims? [15.055733335365847]
  Given a text excerpt referencing a paper, could an LM act as a research assistant to correctly identify the referenced paper? Our benchmark, CiteME, consists of text excerpts from recent machine learning papers, each referencing a single other paper. CiteME use reveals a large gap between frontier LMs and human performance, with LMs achieving only 4.2-18.5% accuracy and humans 69.7%. We close this gap by introducing CiteAgent, an autonomous system built on the GPT-4o LM that can also search and read papers, which achieves an accuracy of 35.3% on CiteME.
  arXiv  Detail & Related papers  (2024-07-10T11:31:20Z)
• CaLM: Contrasting Large and Small Language Models to Verify Grounded Generation [76.31621715032558]
  Grounded generation aims to equip language models (LMs) with the ability to produce more credible and accountable responses. We introduce CaLM, a novel verification framework. Our framework empowers smaller LMs, which rely less on parametric memory, to validate the output of larger LMs.
  arXiv  Detail & Related papers  (2024-06-08T06:04:55Z)
• Large Language Models Reflect Human Citation Patterns with a Heightened Citation Bias [1.7812428873698407]
  Citation practices are crucial in shaping the structure of scientific knowledge. The emergence of Large Language Models (LLMs) like GPT-4 introduces a new dynamic to these practices. Here, we analyze the characteristics and potential biases of references recommended by GPT-4.
  arXiv  Detail & Related papers  (2024-05-24T17:34:32Z)
• Mapping the Increasing Use of LLMs in Scientific Papers [99.67983375899719]
  We conduct the first systematic, large-scale analysis across 950,965 papers published between January 2020 and February 2024 on the arXiv, bioRxiv, and Nature portfolio journals. Our findings reveal a steady increase in LLM usage, with the largest and fastest growth observed in Computer Science papers.
  arXiv  Detail & Related papers  (2024-04-01T17:45:15Z)
• WebCiteS: Attributed Query-Focused Summarization on Chinese Web Search Results with Citations [34.99831757956635]
  We formulate the task of attributed query-focused summarization (AQFS) and present WebCiteS, a Chinese dataset featuring 7k human-annotated summaries with citations. We tackle these issues by developing detailed metrics and enabling the automatic evaluator to decompose the sentences into sub-claims for fine-grained verification.
  arXiv  Detail & Related papers  (2024-03-04T07:06:41Z)
• Enhancing Robustness of LLM-Synthetic Text Detectors for Academic Writing: A Comprehensive Analysis [35.351782110161025]
  Large language models (LLMs) offer numerous advantages in terms of revolutionizing work and study methods. They have also garnered significant attention due to their potential negative consequences. One example is generating academic reports or papers with little to no human contribution.
  arXiv  Detail & Related papers  (2024-01-16T01:58:36Z)
• BooookScore: A systematic exploration of book-length summarization in the era of LLMs [53.42917858142565]
  We develop an automatic metric, BooookScore, that measures the proportion of sentences in a summary that do not contain any of the identified error types. We find that closed-source LLMs such as GPT-4 and 2 produce summaries with higher BooookScore than those generated by open-source models.
  arXiv  Detail & Related papers  (2023-10-01T20:46:44Z)
• Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting [65.00288634420812]
  Pairwise Ranking Prompting (PRP) is a technique to significantly reduce the burden on Large Language Models (LLMs) Our results are the first in the literature to achieve state-of-the-art ranking performance on standard benchmarks using moderate-sized open-sourced LLMs.
  arXiv  Detail & Related papers  (2023-06-30T11:32:25Z)
• Large Language Models are Not Yet Human-Level Evaluators for Abstractive Summarization [66.08074487429477]
  We investigate the stability and reliability of large language models (LLMs) as automatic evaluators for abstractive summarization. We find that while ChatGPT and GPT-4 outperform the commonly used automatic metrics, they are not ready as human replacements.
  arXiv  Detail & Related papers  (2023-05-22T14:58:13Z)
• Assessing Hidden Risks of LLMs: An Empirical Study on Robustness, Consistency, and Credibility [37.682136465784254]
  We conduct over a million queries to the mainstream large language models (LLMs) including ChatGPT, LLaMA, and OPT. We find that ChatGPT is still capable to yield the correct answer even when the input is polluted at an extreme level. We propose a novel index associated with a dataset that roughly decides the feasibility of using such data for LLM-involved evaluation.
  arXiv  Detail & Related papers  (2023-05-15T15:44:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.