@inproceedings{zhu-etal-2025-llm-based,
title = "{LLM}-based Conversational Recommendation Agents with Collaborative Verbalized Experience",
author = "Zhu, Yaochen and
Steck, Harald and
Liang, Dawen and
He, Yinhan and
Kallus, Nathan and
Li, Jundong",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.119/",
doi = "10.18653/v1/2025.findings-emnlp.119",
pages = "2207--2220",
ISBN = "979-8-89176-335-7",
abstract = "Large language models (LLMs) have demonstrated impressive zero-shot capabilities in conversational recommender systems (CRS). However, effectively utilizing historical conversations remains a significant challenge. Current approaches either retrieve few-shot examples or extract global rules to enhance the prompt, which fail to capture the implicit and preference-oriented knowledge. To address this challenge, we propose LLM-based Conversational Recommendation Agents with Collaborative Verbalized Experience, abbreviated as CRAVE. CRAVE begins by sampling trajectories of LLM-based CRS agents on historical queries and establishing verbalized experience banks by reflecting the agents' actions on user feedback. Additionally, we introduce a collaborative retriever network fine-tuned with item content-parameterized multinomial likelihood on query-item pairs to retrieve preference-oriented verbal experiences for new queries. Furthermore, we developed a debater-critic agent (DCA) system where each agent maintains an independent collaborative experience bank and works together to enhance the CRS recommendations. We demonstrate that the open-ended debate and critique nature of DCA benefits significantly from the collaborative experience augmentation with CRAVE. The code is available at https://github.com/yaochenzhu/CRAVE."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="zhu-etal-2025-llm-based">
<titleInfo>
<title>LLM-based Conversational Recommendation Agents with Collaborative Verbalized Experience</title>
</titleInfo>
<name type="personal">
<namePart type="given">Yaochen</namePart>
<namePart type="family">Zhu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Harald</namePart>
<namePart type="family">Steck</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Dawen</namePart>
<namePart type="family">Liang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yinhan</namePart>
<namePart type="family">He</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Nathan</namePart>
<namePart type="family">Kallus</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jundong</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2025-11</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Findings of the Association for Computational Linguistics: EMNLP 2025</title>
</titleInfo>
<name type="personal">
<namePart type="given">Christos</namePart>
<namePart type="family">Christodoulopoulos</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Tanmoy</namePart>
<namePart type="family">Chakraborty</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Carolyn</namePart>
<namePart type="family">Rose</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Violet</namePart>
<namePart type="family">Peng</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Suzhou, China</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-335-7</identifier>
</relatedItem>
<abstract>Large language models (LLMs) have demonstrated impressive zero-shot capabilities in conversational recommender systems (CRS). However, effectively utilizing historical conversations remains a significant challenge. Current approaches either retrieve few-shot examples or extract global rules to enhance the prompt, which fail to capture the implicit and preference-oriented knowledge. To address this challenge, we propose LLM-based Conversational Recommendation Agents with Collaborative Verbalized Experience, abbreviated as CRAVE. CRAVE begins by sampling trajectories of LLM-based CRS agents on historical queries and establishing verbalized experience banks by reflecting the agents’ actions on user feedback. Additionally, we introduce a collaborative retriever network fine-tuned with item content-parameterized multinomial likelihood on query-item pairs to retrieve preference-oriented verbal experiences for new queries. Furthermore, we developed a debater-critic agent (DCA) system where each agent maintains an independent collaborative experience bank and works together to enhance the CRS recommendations. We demonstrate that the open-ended debate and critique nature of DCA benefits significantly from the collaborative experience augmentation with CRAVE. The code is available at https://github.com/yaochenzhu/CRAVE.</abstract>
<identifier type="citekey">zhu-etal-2025-llm-based</identifier>
<identifier type="doi">10.18653/v1/2025.findings-emnlp.119</identifier>
<location>
<url>https://aclanthology.org/2025.findings-emnlp.119/</url>
</location>
<part>
<date>2025-11</date>
<extent unit="page">
<start>2207</start>
<end>2220</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T LLM-based Conversational Recommendation Agents with Collaborative Verbalized Experience
%A Zhu, Yaochen
%A Steck, Harald
%A Liang, Dawen
%A He, Yinhan
%A Kallus, Nathan
%A Li, Jundong
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F zhu-etal-2025-llm-based
%X Large language models (LLMs) have demonstrated impressive zero-shot capabilities in conversational recommender systems (CRS). However, effectively utilizing historical conversations remains a significant challenge. Current approaches either retrieve few-shot examples or extract global rules to enhance the prompt, which fail to capture the implicit and preference-oriented knowledge. To address this challenge, we propose LLM-based Conversational Recommendation Agents with Collaborative Verbalized Experience, abbreviated as CRAVE. CRAVE begins by sampling trajectories of LLM-based CRS agents on historical queries and establishing verbalized experience banks by reflecting the agents’ actions on user feedback. Additionally, we introduce a collaborative retriever network fine-tuned with item content-parameterized multinomial likelihood on query-item pairs to retrieve preference-oriented verbal experiences for new queries. Furthermore, we developed a debater-critic agent (DCA) system where each agent maintains an independent collaborative experience bank and works together to enhance the CRS recommendations. We demonstrate that the open-ended debate and critique nature of DCA benefits significantly from the collaborative experience augmentation with CRAVE. The code is available at https://github.com/yaochenzhu/CRAVE.
%R 10.18653/v1/2025.findings-emnlp.119
%U https://aclanthology.org/2025.findings-emnlp.119/
%U https://doi.org/10.18653/v1/2025.findings-emnlp.119
%P 2207-2220
Markdown (Informal)
[LLM-based Conversational Recommendation Agents with Collaborative Verbalized Experience](https://aclanthology.org/2025.findings-emnlp.119/) (Zhu et al., Findings 2025)
ACL