Scenarios and Approaches for Situated Natural Language Explanations

• URL: http://arxiv.org/abs/2406.05035v1
• Date: Fri, 7 Jun 2024 15:56:32 GMT
• Title: Scenarios and Approaches for Situated Natural Language Explanations
• Authors: Pengshuo Qiu, Frank Rudzicz, Zining Zhu,
• Abstract summary: We collect a benchmarking dataset, Situation-Based Explanation. This dataset contains 100 explanandums. For each "explanandum paired with an audience" situation, we include a human-written explanation. We examine three categories of prompting methods: rule-based prompting, meta-prompting, and in-context learning prompting.
• Score: 18.022428746019582
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) can be used to generate natural language explanations (NLE) that are adapted to different users' situations. However, there is yet to be a quantitative evaluation of the extent of such adaptation. To bridge this gap, we collect a benchmarking dataset, Situation-Based Explanation. This dataset contains 100 explanandums. Each explanandum is paired with explanations targeted at three distinct audience types-such as educators, students, and professionals-enabling us to assess how well the explanations meet the specific informational needs and contexts of these diverse groups e.g. students, teachers, and parents. For each "explanandum paired with an audience" situation, we include a human-written explanation. These allow us to compute scores that quantify how the LLMs adapt the explanations to the situations. On an array of pretrained language models with varying sizes, we examine three categories of prompting methods: rule-based prompting, meta-prompting, and in-context learning prompting. We find that 1) language models can generate prompts that result in explanations more precisely aligned with the target situations, 2) explicitly modeling an "assistant" persona by prompting "You are a helpful assistant..." is not a necessary prompt technique for situated NLE tasks, and 3) the in-context learning prompts only can help LLMs learn the demonstration template but can't improve their inference performance. SBE and our analysis facilitate future research towards generating situated natural language explanations.

Related papers

• Evaluating the Reliability of Self-Explanations in Large Language Models [2.8894038270224867]
  We evaluate two kinds of such self-explanations - extractive and counterfactual. Our findings reveal, that, while these self-explanations can correlate with human judgement, they do not fully and accurately follow the model's decision process. We show that this gap can be bridged because prompting LLMs for counterfactual explanations can produce faithful, informative, and easy-to-verify results.
  arXiv  Detail & Related papers  (2024-07-19T17:41:08Z)
• Can Language Models Explain Their Own Classification Behavior? [1.8177391253202122]
  Large language models (LLMs) perform well at a myriad of tasks, but explaining the processes behind this performance is a challenge. This paper investigates whether LLMs can give faithful high-level explanations of their own internal processes. We release our dataset, ArticulateRules, which can be used to test self-explanation for LLMs trained either in-context or by finetuning.
  arXiv  Detail & Related papers  (2024-05-13T02:31:08Z)
• An Incomplete Loop: Deductive, Inductive, and Abductive Learning in Large Language Models [99.31449616860291]
  Modern language models (LMs) can learn to perform new tasks in different ways. In instruction following, the target task is described explicitly in natural language; in few-shot prompting, the task is specified implicitly. In instruction inference, LMs are presented with in-context examples and are then prompted to generate a natural language task description.
  arXiv  Detail & Related papers  (2024-04-03T19:31:56Z)
• Explanation-aware Soft Ensemble Empowers Large Language Model In-context Learning [50.00090601424348]
  Large language models (LLMs) have shown remarkable capabilities in various natural language understanding tasks. We propose EASE, an Explanation-Aware Soft Ensemble framework to empower in-context learning with LLMs.
  arXiv  Detail & Related papers  (2023-11-13T06:13:38Z)
• Situated Natural Language Explanations [54.083715161895036]
  Natural language explanations (NLEs) are among the most accessible tools for explaining decisions to humans. Existing NLE research perspectives do not take the audience into account. Situated NLE provides a perspective and facilitates further research on the generation and evaluation of explanations.
  arXiv  Detail & Related papers  (2023-08-27T14:14:28Z)
• MaNtLE: Model-agnostic Natural Language Explainer [9.43206883360088]
  We introduce MaNtLE, a model-agnostic natural language explainer that analyzes multiple classifier predictions. MaNtLE uses multi-task training on thousands of synthetic classification tasks to generate faithful explanations. Simulated user studies indicate that, on average, MaNtLE-generated explanations are at least 11% more faithful compared to LIME and Anchors explanations.
  arXiv  Detail & Related papers  (2023-05-22T12:58:06Z)
• Complementary Explanations for Effective In-Context Learning [77.83124315634386]
  Large language models (LLMs) have exhibited remarkable capabilities in learning from explanations in prompts. This work aims to better understand the mechanisms by which explanations are used for in-context learning.
  arXiv  Detail & Related papers  (2022-11-25T04:40:47Z)
• Explaining Patterns in Data with Language Models via Interpretable Autoprompting [143.4162028260874]
  We introduce interpretable autoprompting (iPrompt), an algorithm that generates a natural-language string explaining the data. iPrompt can yield meaningful insights by accurately finding groundtruth dataset descriptions. Experiments with an fMRI dataset show the potential for iPrompt to aid in scientific discovery.
  arXiv  Detail & Related papers  (2022-10-04T18:32:14Z)
• The Unreliability of Explanations in Few-Shot In-Context Learning [50.77996380021221]
  We focus on two NLP tasks that involve reasoning over text, namely question answering and natural language inference. We show that explanations judged as good by humans--those that are logically consistent with the input--usually indicate more accurate predictions. We present a framework for calibrating model predictions based on the reliability of the explanations.
  arXiv  Detail & Related papers  (2022-05-06T17:57:58Z)

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.