Gotcha! Don't trick me with unanswerable questions! Self-aligning Large Language Models for Proactively Responding to Unknown Questions
Authors: Yang Deng, Yong Zhao, Moxin Li, See-Kiong Ng, Tat-Seng Chua


Reviewer paper summaries: This paper proposes a self-align method that uses an LLM itself to enhance the response-ability to four types of unknown questions, which do not have definitive answers due to insufficient or inaccurate information. While existing studies investigate methods for refusing to answer unknown questions, this paper proposes a method for explaining the unanswerability of unknown questions as well as refusing. The self-align method consists of two steps: 1) a base LLM generates a large number of unknown questions and appropriate responses with explanations. Unknown questions are generated by converting answerable questions into a specific type of unknown questions, conditioned on demonstrations. 2) a base LLM evaluates the quality of generated question-response pairs based on the disparity to their known QA pair counterpart. The filtered dataset is used to fine-tune the base LLM. The evaluation is conducted on two datasets and validates the effectiveness of the proposed approach in terms of three types of task formulation, including unknown question detection, unknown question classification, and open-ended response generation.
Summary of strengths: * Explaining the unanswerability of unknown questions is an important task.
* The proposed self-alignment and iteration method for endowing small LLMs with the ability to explain the answerability of unknown questions is sound and novel.
* The comparison to various baselines including in-context learning is thorough and supports the utility of the proposed method.
Summary of weaknesses: * The experiments are conducted with only two LLMs; Vicuna 7B and LLaMA-2 7B. It would be better to incorporate LLMs larger than 7B to see if the findings also hold true for larger models.
* For each type of unknown question in the test sets, the total number is 80, according to Table 1, which is very small. The reported performance improvements on the small test sets may not be statistically significant.
Comments, suggestions and typos: None
Overall assessment scores: 4