Are LLMs Aware that Some Questions are not Open-ended?

• URL: http://arxiv.org/abs/2410.00423v1
• Date: Tue, 1 Oct 2024 06:07:00 GMT
• Title: Are LLMs Aware that Some Questions are not Open-ended?
• Authors: Dongjie Yang, Hai Zhao,
• Abstract summary: We study whether Large Language Models are aware that some questions have limited answers and need to respond more deterministically. The lack of question awareness in LLMs leads to two phenomena: (1) too casual to answer non-open-ended questions or (2) too boring to answer open-ended questions.
• Score: 58.93124686141781
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) have shown the impressive capability of answering questions in a wide range of scenarios. However, when LLMs face different types of questions, it is worth exploring whether LLMs are aware that some questions have limited answers and need to respond more deterministically but some do not. We refer to this as question awareness of LLMs. The lack of question awareness in LLMs leads to two phenomena that LLMs are: (1) too casual to answer non-open-ended questions or (2) too boring to answer open-ended questions. In this paper, we first evaluate the question awareness in LLMs. The experimental results show that LLMs have the issues of lacking awareness of questions in certain domains, e.g. factual knowledge, resulting in hallucinations during the generation. To mitigate these, we propose a method called Question Awareness Temperature Sampling (QuATS). This method enhances the question awareness of LLMs by adaptively adjusting the output distributions based on question features. The automatic adjustment in QuATS eliminates the need for manual temperature tuning in text generation and consistently improves model performance in various benchmarks.

Related papers

• Utilize the Flow before Stepping into the Same River Twice: Certainty Represented Knowledge Flow for Refusal-Aware Instruction Tuning [68.57166425493283]
  We introduce Certainty Represented Knowledge Flow for Refusal-Aware Instructions Construction (CRaFT) CRaFT incorporates response certainty to selectively filter and modify data, reducing static conflicts. We conducted extensive experiments on open-ended question answering and multiple-choice question task.
  arXiv  Detail & Related papers  (2024-10-09T14:12:51Z)
• Open-LLM-Leaderboard: From Multi-choice to Open-style Questions for LLMs Evaluation, Benchmark, and Arena [23.264049073539663]
  Multiple-choice questions (MCQ) are frequently used to assess large language models (LLMs) LLMs may inherently favor certain answer choice IDs, such as A/B/C/D, due to inherent biases of priori unbalanced probabilities. This work aims to tackle these significant difficulties, and establish a new LLM evaluation benchmark through entirely open-style questions.
  arXiv  Detail & Related papers  (2024-06-11T17:59:47Z)
• Perception of Knowledge Boundary for Large Language Models through Semi-open-ended Question Answering [67.94354589215637]
  Large Language Models (LLMs) are widely used for knowledge-seeking yet suffer from hallucinations. In this paper, we perceive the LLMs' knowledge boundary (KB) with semi-open-ended questions (SoeQ) We find that GPT-4 performs poorly on SoeQ and is often unaware of its KB. Our auxiliary model, LLaMA-2-13B, is effective in discovering more ambiguous answers.
  arXiv  Detail & Related papers  (2024-05-23T10:00:14Z)
• When Do LLMs Need Retrieval Augmentation? Mitigating LLMs' Overconfidence Helps Retrieval Augmentation [66.01754585188739]
  Large Language Models (LLMs) have been found to have difficulty knowing they do not possess certain knowledge. Retrieval Augmentation (RA) has been extensively studied to mitigate LLMs' hallucinations. We propose several methods to enhance LLMs' perception of knowledge boundaries and show that they are effective in reducing overconfidence.
  arXiv  Detail & Related papers  (2024-02-18T04:57:19Z)
• Learn to Refuse: Making Large Language Models More Controllable and Reliable through Knowledge Scope Limitation and Refusal Mechanism [0.0]
  Large language models (LLMs) have demonstrated impressive language understanding and generation capabilities. These models are not flawless and often produce responses that contain errors or misinformation. We propose a refusal mechanism that instructs LLMs to refuse to answer challenging questions in order to avoid errors.
  arXiv  Detail & Related papers  (2023-11-02T07:20:49Z)
• Knowing What LLMs DO NOT Know: A Simple Yet Effective Self-Detection Method [36.24876571343749]
  Large Language Models (LLMs) have shown great potential in Natural Language Processing (NLP) tasks. Recent literature reveals that LLMs generate nonfactual responses intermittently. We propose a novel self-detection method to detect which questions that a LLM does not know that are prone to generate nonfactual results.
  arXiv  Detail & Related papers  (2023-10-27T06:22:14Z)
• FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation [92.43001160060376]
  We study the factuality of large language models (LLMs) in the context of answering questions that test current world knowledge. We introduce FreshQA, a novel dynamic QA benchmark encompassing a diverse range of question and answer types. We benchmark a diverse array of both closed and open-source LLMs under a two-mode evaluation procedure that allows us to measure both correctness and hallucination. Motivated by these results, we present FreshPrompt, a simple few-shot prompting method that substantially boosts the performance of an LLM on FreshQA.
  arXiv  Detail & Related papers  (2023-10-05T00:04:12Z)
• Investigating Answerability of LLMs for Long-Form Question Answering [35.41413072729483]
  We focus on long-form question answering (LFQA) because it has several practical and impactful applications. We propose a question-generation method from abstractive summaries and show that generating follow-up questions from summaries of long documents can create a challenging setting.
  arXiv  Detail & Related papers  (2023-09-15T07:22:56Z)
• Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback [127.75419038610455]
  Large language models (LLMs) are able to generate human-like, fluent responses for many downstream tasks. This paper proposes a LLM-Augmenter system, which augments a black-box LLM with a set of plug-and-play modules.
  arXiv  Detail & Related papers  (2023-02-24T18:48:43Z)

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.