---
title: "Sibyl — paper by Wang et al. (2025) — summary and ideas"
description: "The authors argue that existing inference tools such as COMET and DIALeCT look only at past turns. 51 references, cited by 8 papers here."
authors: ["Lanrui Wang", "Jiangnan Li", "Chenxu Yang", "Zheng Lin", "Hongyin Tang", "Huan Liu", "Yanan Cao", "Jingang Wang", "Weiping Wang"]
year: 2025
venue: "Proceedings of the 31st International Conference on Computational Linguistics"
source: "https://aclanthology.org/2025.coling-main.10/"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/wang-2025-sibyl-empowering-empathetic-dialogue-generation/"
---
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# Sibyl: Empowering Empathetic Dialogue Generation in Large Language Models via Sensible and Visionary Commonsense Inference

**Authors:** Lanrui Wang, [Jiangnan Li](https://md.research.elamur.ai/free/en/authors/jiangnan-li/), [Chenxu Yang](https://md.research.elamur.ai/free/en/authors/chenxu-yang/), [Zheng Lin](https://md.research.elamur.ai/free/en/authors/zheng-lin/), Hongyin Tang, [Huan Liu](https://md.research.elamur.ai/free/en/authors/huan-liu/), [Yanan Cao](https://md.research.elamur.ai/free/en/authors/yanan-cao/), [Jingang Wang](https://md.research.elamur.ai/free/en/authors/jingang-wang/) and [Weiping Wang](https://md.research.elamur.ai/free/en/authors/weiping-wang/) · **Year:** 2025 · **Venue:** [Proceedings of the 31st International Conference on Computational Linguistics](https://md.research.elamur.ai/free/en/venues/coling/) ([COLING 2025](https://md.research.elamur.ai/free/en/venues/coling/2025/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2025.coling-main.10/) · [PDF](https://aclanthology.org/2025.coling-main.10.pdf) · [OpenAlex](https://openalex.org/W4389115869)

**Citations:** 1 citations (OpenAlex, 2026-10-14) · [8](https://md.research.elamur.ai/free/en/papers/wang-2025-sibyl-empowering-empathetic-dialogue-generation/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
LLM-based chatbots often fall short on empathy and emotional support in multi-turn conversations, even when they are given commonsense knowledge. The authors argue that existing inference tools such as COMET and DIALeCT look only at past turns. This yields knowledge that is too narrow or irrelevant to where the conversation should go next, because many different replies can fit the same context. Their framework, Sibyl, instead produces "visionary" commonsense aimed at the upcoming turn. A strong LLM first reads both the dialogue history and the gold reply and writes four kinds of inference: cause, subsequent event, the user's emotion and the assistant's next intent. LLaMA models are then trained on these outputs so they can produce the same inferences from the history alone. The inferences work as chain-of-thought-style hints for the response generator. On EmpatheticDialogues and ESConv, Sibyl beats competing commonsense sources in automatic metrics, G-Eval judgments and human ratings. The approach is model-agnostic, so it offers a cheap way to make dialogue agents more supportive.

## Key points
- **Four inference types:** Cause, Subsequent Event, Emotion state and Intention. The ablation shows that removing any one lowers overall quality. Intent matters most, while causality contributes least.
- **Distillation setup:** a powerful LLM uses both the history and the ground-truth reply to create the knowledge. Fine-tuned LLaMA models learn to reproduce it from the history only.
- **Baselines and evaluation:** Sibyl is compared with COMET, DIALeCT and DOCTOR knowledge. G-Eval was run on 200 sampled examples per dataset, scoring Naturalness, Coherence, and either Empathy (ED) or Supportiveness (ESConv). Sibyl came out ahead on every aspect.
- **Small models:** simply appending Sibyl knowledge to Normal Transformer, Blenderbot-small-90M and Bart-base was enough to surpass CASE and EmpSOA on ED, and MISC and MultiESC on ESConv. Those baselines rely on dedicated knowledge encoders.
- **Availability:** the code is released at github.com/wlr737/Sibyl. The paper was published in COLING 2025, pp. 123–140.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Lanrui Wang, Jiangnan Li, Chenxu Yang, Zheng Lin, Hongyin Tang, Huan Liu, Yanan Cao, Jingang Wang and Weiping Wang. 2025. Sibyl: Empowering Empathetic Dialogue Generation in Large Language Models via Sensible and Visionary Commonsense Inference. Proceedings of the 31st International Conference on Computational Linguistics.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{wang2025sibyl,
  title = {{Sibyl: Empowering Empathetic Dialogue Generation in Large Language Models via Sensible and Visionary Commonsense Inference}},
  author = {Lanrui Wang, Jiangnan Li, Chenxu Yang, Zheng Lin, Hongyin Tang, Huan Liu, Yanan Cao, Jingang Wang and Weiping Wang},
  booktitle = {Proceedings of the 31st International Conference on Computational Linguistics},
  year = {2025},
  url = {https://aclanthology.org/2025.coling-main.10/}
}
```

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*Updated 2 November 2026*