Aligning Language Models to Explicitly Handle Ambiguity
- URL: http://arxiv.org/abs/2404.11972v2
- Date: Mon, 17 Jun 2024 03:04:32 GMT
- Title: Aligning Language Models to Explicitly Handle Ambiguity
- Authors: Hyuhng Joon Kim, Youna Kim, Cheonbok Park, Junyeob Kim, Choonghyun Park, Kang Min Yoo, Sang-goo Lee, Taeuk Kim,
- Abstract summary: We propose Alignment with Perceived Ambiguity (APA), a novel pipeline that aligns language models to deal with ambiguous queries.
We show that APA empowers LLMs to explicitly detect and manage ambiguous queries while retaining the ability to answer clear questions.
- Score: 22.078095273053506
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In interactions between users and language model agents, user utterances frequently exhibit ellipsis (omission of words or phrases) or imprecision (lack of exactness) to prioritize efficiency. This can lead to varying interpretations of the same input based on different assumptions or background knowledge. It is thus crucial for agents to adeptly handle the inherent ambiguity in queries to ensure reliability. However, even state-of-the-art large language models (LLMs) still face challenges in such scenarios, primarily due to the following hurdles: (1) LLMs are not explicitly trained to deal with ambiguous utterances; (2) the degree of ambiguity perceived by the LLMs may vary depending on the possessed knowledge. To address these issues, we propose Alignment with Perceived Ambiguity (APA), a novel pipeline that aligns LLMs to manage ambiguous queries by leveraging their own assessment of ambiguity (i.e., perceived ambiguity). Experimental results on question-answering datasets demonstrate that APA empowers LLMs to explicitly detect and manage ambiguous queries while retaining the ability to answer clear questions. Furthermore, our finding proves that APA excels beyond training with gold-standard labels, especially in out-of-distribution scenarios.
Related papers
- Behavioral Testing: Can Large Language Models Implicitly Resolve Ambiguous Entities? [27.10502683001428]
We analyze current state-of-the-art large language models (LLMs) for their proficiency and consistency in applying their factual knowledge when prompted for entities under ambiguity.
Experiments reveal that LLMs perform poorly with ambiguous prompts, achieving only 80% accuracy.
arXiv Detail & Related papers (2024-07-24T09:48:48Z) - Understanding the Relationship between Prompts and Response Uncertainty in Large Language Models [55.332004960574004]
Large language models (LLMs) are widely used in decision-making, but their reliability, especially in critical tasks like healthcare, is not well-established.
This paper investigates how the uncertainty of responses generated by LLMs relates to the information provided in the input prompt.
We propose a prompt-response concept model that explains how LLMs generate responses and helps understand the relationship between prompts and response uncertainty.
arXiv Detail & Related papers (2024-07-20T11:19:58Z) - 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) - Can Large Language Models Identify Authorship? [18.378744138365537]
Large Language Models (LLMs) have demonstrated exceptional capacity for reasoning and problem-solving.
This paper conducts a comprehensive evaluation of LLMs in authorship analysis.
arXiv Detail & Related papers (2024-03-13T03:22:02Z) - FAC$^2$E: Better Understanding Large Language Model Capabilities by
Dissociating Language and Cognition [57.747888532651]
Large language models (LLMs) are primarily evaluated by overall performance on various text understanding and generation tasks.
We present FAC$2$E, a framework for Fine-grAined and Cognition-grounded LLMs' Capability Evaluation.
arXiv Detail & Related papers (2024-02-29T21:05:37Z) - 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) - 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) - Simple Linguistic Inferences of Large Language Models (LLMs): Blind Spots and Blinds [59.71218039095155]
We evaluate language understanding capacities on simple inference tasks that most humans find trivial.
We target (i) grammatically-specified entailments, (ii) premises with evidential adverbs of uncertainty, and (iii) monotonicity entailments.
The models exhibit moderate to low performance on these evaluation sets.
arXiv Detail & Related papers (2023-05-24T06:41:09Z) - We're Afraid Language Models Aren't Modeling Ambiguity [136.8068419824318]
Managing ambiguity is a key part of human language understanding.
We characterize ambiguity in a sentence by its effect on entailment relations with another sentence.
We show that a multilabel NLI model can flag political claims in the wild that are misleading due to ambiguity.
arXiv Detail & Related papers (2023-04-27T17:57:58Z)
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.