# Measuring Bargaining Abilities of LLMs: A Benchmark and A Buyer-Enhancement Method

arXiv: https://arxiv.org/abs/2402.15813 · HTML: https://mlsanity.com/paper/2402.15813/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 37 references that are in the map.
- First posted on arXiv 24 Feb 2024.

## Builds on (most cited first, 19 of 19)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
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- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents](https://mlsanity.com/paper/2207.01206/) (2022)
- [ALFWorld: Aligning Text and Embodied Environments for Interactive Learning](https://mlsanity.com/paper/2010.03768/) (2020)
- [Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation](https://mlsanity.com/paper/2108.12409/) (2021)
- [GLM: General Language Model Pretraining with Autoregressive Blank Infilling](https://mlsanity.com/paper/2103.10360/) (2021)
- [Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate](https://mlsanity.com/paper/2305.19118/) (2023)
- [R-Judge: Benchmarking Safety Risk Awareness for LLM Agents](https://mlsanity.com/paper/2401.10019/) (2024)
- [Deal or No Deal? End-to-End Learning for Negotiation Dialogues](https://mlsanity.com/paper/1706.05125/) (2017)
- [Improving Language Model Negotiation with Self-Play and In-Context Learning from AI Feedback](https://mlsanity.com/paper/2305.10142/) (2023)
- [Decoupling Strategy and Generation in Negotiation Dialogues](https://mlsanity.com/paper/1808.09637/) (2018)
- [Put Your Money Where Your Mouth Is: Evaluating Strategic Planning and Execution of LLM Agents in an Auction Arena](https://mlsanity.com/paper/2310.05746/) (2023)
- [Igniting Language Intelligence: The Hitchhiker's Guide From Chain-of-Thought Reasoning to Language Agents](https://mlsanity.com/paper/2311.11797/) (2023)
- [Improving Machine Translation with Human Feedback: An Exploration of Quality Estimation as a Reward Model](https://mlsanity.com/paper/2401.12873/) (2024)
- [A Dynamic Strategy Coach for Effective Negotiation](https://mlsanity.com/paper/1909.13426/) (2019)
- [Is Self-knowledge and Action Consistent or Not: Investigating Large Language Model's Personality](https://mlsanity.com/paper/2402.14679/) (2024)
- [Towards a Neural Era in Dialogue Management for Collaboration: A Literature Survey](https://mlsanity.com/paper/2307.09021/) (2023)

## Built on by (most cited first, 30 of 48)

- [Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems](https://mlsanity.com/paper/2504.01990/) (2025)
- [LLM as a Mastermind: A Survey of Strategic Reasoning with Large Language Models](https://mlsanity.com/paper/2404.01230/) (2024)
- [From Individual to Society: A Survey on Social Simulation Driven by Large Language Model-based Agents](https://mlsanity.com/paper/2412.03563/) (2024)
- [Game-theoretic LLM: Agent Workflow for Negotiation Games](https://mlsanity.com/paper/2411.05990/) (2024)
- [Richelieu: Self-Evolving LLM-Based Agents for AI Diplomacy](https://mlsanity.com/paper/2407.06813/) (2024)
- [A Survey on Large Language Model-Based Social Agents in Game-Theoretic Scenarios](https://mlsanity.com/paper/2412.03920/) (2024)
- [GLEE: A Unified Framework and Benchmark for Language-based Economic Environments](https://mlsanity.com/paper/2410.05254/) (2024)
- [Game Theory Meets Large Language Models: A Systematic Survey with Taxonomy and New Frontiers](https://mlsanity.com/paper/2502.09053/) (2025)
- [AgenticPay: A Multi-Agent LLM Negotiation System for Buyer-Seller Transactions](https://mlsanity.com/paper/2602.06008/) (2026)
- [Strategic Tradeoffs Between Humans and AI in Multi-Agent Bargaining](https://mlsanity.com/paper/2509.09071/) (2025)
- [Debt Collection Negotiations with Large Language Models: An Evaluation System and Optimizing Decision Making with Multi-Agent](https://mlsanity.com/paper/2502.18228/) (2025)
- [Evaluating & Reducing Deceptive Dialogue From Language Models with Multi-turn RL](https://mlsanity.com/paper/2510.14318/) (2025)
- [Beyond Nash Equilibrium: Bounded Rationality of LLMs and humans in Strategic Decision-making](https://mlsanity.com/paper/2506.09390/) (2025)
- [TERMS-Bench: Diagnosing LLM Negotiation Agents Beyond Deal Rate](https://mlsanity.com/paper/2605.13909/) (2026)
- [How Does Cognitive Bias Affect Large Language Models? A Case Study on the Anchoring Effect in Price Negotiation Simulations](https://mlsanity.com/paper/2508.21137/) (2025)
- [Instructing LLMs to Negotiate using Reinforcement Learning with Verifiable Rewards](https://mlsanity.com/paper/2604.09855/) (2026)
- [Advancing AI Negotiations: A Large-Scale Autonomous Negotiation Competition](https://mlsanity.com/paper/2503.06416/) (2025)
- [CreDes: Causal Reasoning Enhancement and Dual-End Searching for Solving Long-Range Reasoning Problems using LLMs](https://mlsanity.com/paper/2410.01696/) (2024)
- [Agent-to-Agent Theory of Mind: Testing Interlocutor Awareness among Large Language Models](https://mlsanity.com/paper/2506.22957/) (2025)
- [Real-Time Deadlines Reveal Fragile Temporal Adaptation in LLM Strategic Dialogues](https://mlsanity.com/paper/2601.13206/) (2026)
- [Training Language Models for Bilateral Trade with Private Information](https://mlsanity.com/paper/2604.16472/) (2026)
- [ASTRA: A Negotiation Agent with Adaptive and Strategic Reasoning via Tool-integrated Action for Dynamic Offer Optimization](https://mlsanity.com/paper/2503.07129/) (2025)
- [LLM Agents for Bargaining with Utility-based Feedback](https://mlsanity.com/paper/2505.22998/) (2025)
- [On the Adaptive Psychological Persuasion of Large Language Models](https://mlsanity.com/paper/2506.06800/) (2025)
- [Evaluating Multi-Turn Bargain Skills in LLM-Based Seller Agent](https://mlsanity.com/paper/2509.06341/) (2025)
- [PoliCon: Evaluating LLMs on Achieving Diverse Political Consensus Objectives](https://mlsanity.com/paper/2505.19558/) (2025)
- [MERIT Feedback Elicits Better Bargaining in LLM Negotiators](https://mlsanity.com/paper/2602.10467/) (2026)
- [What Makes a Sale? Simulating End-to-End Seller--Buyer Retail Dynamics with LLM Agents](https://mlsanity.com/paper/2604.04468/) (2026)
- [MedConceal: A Benchmark for Clinical Hidden-Concern Reasoning Under Partial Observability](https://mlsanity.com/paper/2604.08788/) (2026)
- [LLM Agents for Deliberative Collaboration: A Study on Joint Decision Making Under Partial Observability](https://mlsanity.com/paper/2607.06157/) (2026)

## Abstract (arXiv)

Bargaining is an important and unique part of negotiation between humans. As LLM-driven agents learn to negotiate and act like real humans, how to evaluate agents' bargaining abilities remains an open problem. For the first time, we formally described the Bargaining task as an asymmetric incomplete information game, defining the gains of the Buyer and Seller in multiple bargaining processes. It allows us to quantitatively assess an agent's performance in the Bargain task. We collected a real product price dataset, AmazonHistoryPrice, and conducted evaluations of various LLM agents' bargaining abilities. We find that playing a Buyer is much harder than a Seller, and increasing model size can not effectively improve the Buyer's performance. To address the challenge, we propose a novel approach called OG-Narrator that integrates a deterministic Offer Generator to control the price range of Buyer's offers, and an LLM Narrator to create natural language sentences for generated offers. Experimental results show that OG-Narrator improves the buyer's deal rates from 26.67% to 88.88% and brings a ten times multiplication of profits on all baselines, even a model that has not been aligned.