Context Matters: Data-Efficient Augmentation of Large Language Models for Scientific Applications

• URL: http://arxiv.org/abs/2312.07069v2
• Date: Thu, 21 Dec 2023 09:47:19 GMT
• Title: Context Matters: Data-Efficient Augmentation of Large Language Models for Scientific Applications
• Authors: Xiang Li, Haoran Tang, Siyu Chen, Ziwei Wang, Anurag Maravi, Marcin Abram
• Abstract summary: We explore the challenges inherent to Large Language Models (LLMs) like GPT-4. The capacity of LLMs to present erroneous answers in a coherent and semantically rigorous manner complicates the detection of factual inaccuracies. Our work aims to enhance the understanding and mitigation of such errors, thereby contributing to the improvement of LLM accuracy and reliability.
• Score: 15.893290942177112
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we explore the challenges inherent to Large Language Models (LLMs) like GPT-4, particularly their propensity for hallucinations, logic mistakes, and incorrect conclusions when tasked with answering complex questions. The capacity of LLMs to present erroneous answers in a coherent and semantically rigorous manner further complicates the detection of factual inaccuracies. This issue is especially pronounced in fields that require specialized expertise. Our work delves into these challenges, aiming to enhance the understanding and mitigation of such errors, thereby contributing to the improvement of LLM accuracy and reliability in scientific and other specialized domains. Our findings reveal a non-linear relationship between the context's relevancy and the answers' measured quality. In addition, we demonstrate that with the correct calibration, it is possible to automate the grading procedure -- a finding suggesting that, at least to some degree, the LLMs can be used to self-examine the quality of their own performance. Finally, we describe an experimental platform that can be seen as a proof-of-concept of the techniques described in this work.

Related papers

• A Debate-Driven Experiment on LLM Hallucinations and Accuracy [7.821303946741665]
  This study investigates the phenomenon of hallucination in large language models (LLMs) Multiple instances of GPT-4o-Mini models engage in a debate-like interaction prompted with questions from the TruthfulQA dataset. One model is deliberately instructed to generate plausible but false answers while the other models are asked to respond truthfully.
  arXiv  Detail & Related papers  (2024-10-25T11:41:27Z)
• Deconfounded Causality-aware Parameter-Efficient Fine-Tuning for Problem-Solving Improvement of LLMs [12.48241058167222]
  Large Language Models (LLMs) have demonstrated remarkable efficiency in tackling various tasks based on human instructions. But studies reveal that they often struggle with tasks requiring reasoning, such as math or physics limitation. This raises questions about whether LLMs truly comprehend embedded knowledge or merely learn to replicate the token distribution without a true understanding of the content. We propose Decon Causal Adaptation (DCA), a novel parameter-efficient fine-tuning (PEFT) method to enhance the model's reasoning capabilities.
  arXiv  Detail & Related papers  (2024-09-04T13:17:09Z)
• Exposing the Achilles' Heel: Evaluating LLMs Ability to Handle Mistakes in Mathematical Reasoning [11.63133816413199]
  Large Language Models (LLMs) have been applied to Math Word Problems (MWPs) We introduce a novel dataset MWP-MISTAKE, incorporating MWPs with both correct and incorrect reasoning steps generated through rule-based methods and smaller language models. We highlight GPT-$o's superior performance in mistake detection and rectification and the persistent challenges faced by smaller models.
  arXiv  Detail & Related papers  (2024-06-16T08:06:05Z)
• LLMs' Reading Comprehension Is Affected by Parametric Knowledge and Struggles with Hypothetical Statements [59.71218039095155]
  Task of reading comprehension (RC) provides a primary means to assess language models' natural language understanding (NLU) capabilities. If the context aligns with the models' internal knowledge, it is hard to discern whether the models' answers stem from context comprehension or from internal information. To address this issue, we suggest to use RC on imaginary data, based on fictitious facts and entities.
  arXiv  Detail & Related papers  (2024-04-09T13:08:56Z)
• Uncertainty Quantification for In-Context Learning of Large Language Models [52.891205009620364]
  In-context learning has emerged as a groundbreaking ability of Large Language Models (LLMs) We propose a novel formulation and corresponding estimation method to quantify both types of uncertainties. The proposed method offers an unsupervised way to understand the prediction of in-context learning in a plug-and-play fashion.
  arXiv  Detail & Related papers  (2024-02-15T18:46:24Z)
• A Comprehensive Study of Knowledge Editing for Large Language Models [82.65729336401027]
  Large Language Models (LLMs) have shown extraordinary capabilities in understanding and generating text that closely mirrors human communication. This paper defines the knowledge editing problem and provides a comprehensive review of cutting-edge approaches. We introduce a new benchmark, KnowEdit, for a comprehensive empirical evaluation of representative knowledge editing approaches.
  arXiv  Detail & Related papers  (2024-01-02T16:54:58Z)
• Competition-Level Problems are Effective LLM Evaluators [121.15880285283116]
  This paper aims to evaluate the reasoning capacities of large language models (LLMs) in solving recent programming problems in Codeforces. We first provide a comprehensive evaluation of GPT-4's peiceived zero-shot performance on this task, considering various aspects such as problems' release time, difficulties, and types of errors encountered. Surprisingly, theThoughtived performance of GPT-4 has experienced a cliff like decline in problems after September 2021 consistently across all the difficulties and types of problems.
  arXiv  Detail & Related papers  (2023-12-04T18:58:57Z)
• You don't need a personality test to know these models are unreliable: Assessing the Reliability of Large Language Models on Psychometric Instruments [37.03210795084276]
  We examine whether the current format of prompting Large Language Models elicits responses in a consistent and robust manner. Our experiments on 17 different LLMs reveal that even simple perturbations significantly downgrade a model's question-answering ability. Our results suggest that the currently widespread practice of prompting is insufficient to accurately and reliably capture model perceptions.
  arXiv  Detail & Related papers  (2023-11-16T09:50:53Z)
• Large Language Models Cannot Self-Correct Reasoning Yet [78.16697476530994]
  Large Language Models (LLMs) have emerged as a groundbreaking technology with their unparalleled text generation capabilities. Concerns persist regarding the accuracy and appropriateness of their generated content. A contemporary methodology, self-correction, has been proposed as a remedy to these issues.
  arXiv  Detail & Related papers  (2023-10-03T04:56:12Z)
• Automatically Correcting Large Language Models: Surveying the landscape of diverse self-correction strategies [104.32199881187607]
  Large language models (LLMs) have demonstrated remarkable performance across a wide array of NLP tasks. A promising approach to rectify these flaws is self-correction, where the LLM itself is prompted or guided to fix problems in its own output. This paper presents a comprehensive review of this emerging class of techniques.
  arXiv  Detail & Related papers  (2023-08-06T18:38:52Z)

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.