Large language models (LLMs) are currently effective at answering questions
that are clearly asked. However, when faced with ambiguous queries they can act
unpredictably and produce incorrect outputs. This underscores the need for the
development of intelligent agents capable of asking clarification questions to
resolve ambiguities effectively. This capability requires complex
understanding, state tracking, reasoning and planning over multiple
conversational turns. However, directly measuring this can be challenging. In
this paper, we offer a surrogate problem which assesses an LLMs's capability to
deduce an entity unknown to itself, but revealed to a judge, by asking the
judge a series of queries. This \textit{entity-deducing game} can serve as an
evaluation framework to probe the conversational reasoning and planning
capabilities of language models. We systematically evaluate various LLMs and
discover significant differences in their performance on this task. We find
that strong LLMs like GPT-4 outperform human players by a large margin. We
further employ Behavior Cloning (BC) to examine whether a weaker model is
capable of imitating a stronger model and generalizing to data or domains,
using only the demonstrations from a stronger model. We finally propose to use
Reinforcement Learning to enhance reasoning and planning capacity of Vicuna
models through episodes of game playing, which lead to significant performance
improvement. We hope that this problem offers insights into how autonomous
agents could be trained to behave more intelligently in ambiguous
circumstances.

大语言模型在回答清楚的问题方面非常有效，但面对模糊查询时会表现不可预测且产生错误的输出，因此需要开发能够提出澄清问题来解决模糊性的智能代理。本文提供了一个评估框架，通过问法官一系列问题来推断一个未知的实体并评估语言模型的会话推理和规划能力，并对不同的语言模型进行系统评估。结果发现，像 GPT-4 这样的强大语言模型在这个任务上远远超过人类玩家。同时我们还使用行为克隆（BC）来研究较弱模型是否能够模仿强模型并在只使用强模型的演示数据或领域的情况下进行泛化。最后，我们建议使用强化学习来通过游戏过程来提高 Vicuna 模型的推理和规划能力，从而显著提高性能。希望这个问题能为如何训练自主代理在模糊环境中更智能地行为提供见解。

实体演绎竞技场：探索 LLM 的对话推理和规划能力的游乐场

The Entity-Deduction Arena: A playground for probing the conversational  reasoning and planning capabilities of LLMs

Dialogue systems can leverage large pre-trained language models and knowledge
to generate fluent and informative responses. However, these models are still
prone to produce hallucinated responses not supported by the input source,
which greatly hinders their application. The heterogeneity between external
knowledge and dialogue context challenges representation learning and source
integration, and further contributes to unfaithfulness. To handle this
challenge and generate more faithful responses, this paper presents RHO
($\rho$) utilizing the representations of linked entities and relation
predicates from a knowledge graph (KG). We propose (1) local knowledge
grounding to combine textual embeddings with the corresponding KG embeddings;
and (2) global knowledge grounding to equip RHO with multi-hop reasoning
abilities via the attention mechanism. In addition, we devise a response
re-ranking technique based on walks over KG sub-graphs for better
conversational reasoning. Experimental results on OpenDialKG show that our
approach significantly outperforms state-of-the-art methods on both automatic
and human evaluation by a large margin, especially in hallucination reduction
(17.54% in FeQA).

本文提出了结合知识图谱中的联通实体和关系谓词表达方式，通过局部和全局知识接地的方式建立起区分模型的关联模型并进行实验验证。结果表明，在减少幻觉方面取得了显著成果 (FeQA 中 17.54％的提高)，更好的内部转换推理技术和对话推理技术。