Despite the recent advancements in Large Language Models (LLMs), which have significantly enhanced the generative capabilities for various NLP tasks, LLMs still face limitations in directly handling retrieval tasks. However, many practical applications demand the seamless integration of both retrieval and generation. This paper introduces a novel and efficient One-pass Generation and retrieval framework (OneGen), designed to improve LLMs' performance on tasks that require both generation and retrieval. The proposed framework bridges the traditionally separate training approaches for generation and retrieval by incorporating retrieval tokens generated autoregressively. This enables a single LLM to handle both tasks simultaneously in a unified forward pass. We conduct experiments on two distinct types of composite tasks, RAG and Entity Linking, to validate the pluggability, effectiveness, and efficiency of OneGen in training and inference. Furthermore, our results show that integrating generation and retrieval within the same context preserves the generative capabilities of LLMs while improving retrieval performance. To the best of our knowledge, OneGen is the first to enable LLMs to conduct vector retrieval during the generation.

OneGen: Efficient One-Pass Unified Generation and Retrieval for LLMs

As Large Language Models (LLMs) and multi-agent systems are increasingly employed in intricate social environments, the research on how human interact with LLMs/agents become promising. This study aims to explore the dynamics of Human-AI interaction through a case study of the Diplomacy game, a strategic board game renowned for its emphasis on negotiation and alliance formation. The research investigates how agents and human influence each other on decision-making, trust, and strategy within interactive settings, specifically focusing on whether agents can mimic human-like strategic behaviors including deception, and how this affects human player perceptions and actions. To this end, we propose to utilize LLMs to play the game with integrated natural language processing capabilities for negotiating and strategizing. The experimental design\footnote{As this extended abstract is a position paper, we haven't yet provide experimental results but some tentative design.} will involve mixed groups of human and AI players, allowing for the analysis of game dynamics across different team configurations.

Exploring Human-AI Interaction: A Case Study on the Diplomacy Game

As Natural Language Processing (NLP) systems are increasingly employed in intricate social environments, a pressing query emerges: *Can these NLP systems mirror human-esque collaborative intelligence, in a multi-agent society consisting of multiple large language models (LLMs)?* 
This paper probes the collaboration mechanisms among contemporary NLP systems by melding practical experiments with theoretical insights. We fabricate four unique 'societies' comprised of LLM agents, where each agent is characterized by a specific 'trait' (easy-going or overconfident) and engages in collaboration with a distinct 'thinking pattern' (debate or reflection). 
Through evaluating these multi-agent societies on three benchmark datasets, we discern that certain collaborative strategies not only outshine previous top-tier approaches but also optimize efficiency (using fewer API tokens). 
Moreover, our results further illustrate that LLM agents manifest human-like social behaviors, such as conformity and consensus reaching, mirroring foundational social psychology theories. 
In conclusion, we integrate insights from social psychology to contextualize the collaboration of LLM agents, inspiring further investigations into the collaboration mechanism for LLMs. 
We commit to sharing our code and datasets\footnote{\url{https://github.com/zjunlp/MachineSoM}.}, hoping to catalyze further research in this promising avenue.

Exploring Collaboration Mechanisms for LLM Agents: A Social Psychology View

This poster session includes Main Conference posters and Findings from the following areas:

Special Theme: Efficiency in Model Algorithms, Training, and Inference  • Summarization • Sentiment Analysis, Stylistic Analysis, and Argument Mining

In-Person Poster Session C (Jasmine)

poster

## Welcome to EMNLP 2024! 
We are excited to welcome you to one of the most prominent conferences in the field of Natural Language Processing. This year, EMNLP 2024 is being held in a hybrid format,
offering both virtual and in-person participation in beautiful Miami. Due to a record-breaking number of submissions, we've expanded the total number of accepted papers to accommodate more cutting-edge research from around the globe.
### [Conference Handbook](https://drive.google.com/file/d/1WPROgxjLAC96AJL7Ugy0tEnYm7dkrbHt/view?usp=sharing)

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EMNLP 2024

EMNLP 2024 will take place in Miami, Florida from Nov 12th to Nov 16th, 2024, at the Hyatt Regency Miami Hote and on Underline for remote participants.

*Coffee Breaks will be provided between 10:30 and 11:00 and 15:30 and 16:00.* To view the workshop program in detail, click [here](https://hucllm-workshop.github.io)

W3: The 1st Human-Centered Large Language Modeling workshop (HuCLLM)

workshop paper

### Welcome!
The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. Our Virtual Poster Sessions will take place online Thursday, August 22, 2024.

You are required to register for this event. **Please register [here](https://2024.aclweb.org/registration). **

If you have already registered, please check your inbox for an email from Underline granting you access to ACL 2024 content.

ACL 2024

The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. More information will be announced soon.

Jintian Zhang

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3

Presentations

OneGen: Efficient One-Pass Unified Generation and Retrieval for LLMs

Exploring Human-AI Interaction: A Case Study on the Diplomacy Game

Exploring Collaboration Mechanisms for LLM Agents: A Social Psychology View

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