Old Habits Die Hard: How Conversational History Geometrically Traps LLMs
arXiv:2603.03308v1 Announce Type: cross Abstract: How does the conversational past of large language models (LLMs) influence their future performance? Recent work suggests that LLMs are affected by their conversational history in unexpected ways. For instance, hallucinations in prior interactions may influence subsequent model responses. In this work, we introduce History-Echoes, a framework that investigates how conversational history biases subsequent generations. The framework explores this bias from two perspectives: probabilistically, we model conversations as Markov chains to quantify state consistency; geometrically, we measure the consistency of consecutive hidden representations. Across three model families and six datasets spanning diverse phenomena, our analysis reveals a strong correlation between the two perspectives. By bridging these perspectives, we demonstrate that behavioral persistence manifests as a geometric trap, where gaps in the latent space confine the model's
arXiv:2603.03308v1 Announce Type: cross Abstract: How does the conversational past of large language models (LLMs) influence their future performance? Recent work suggests that LLMs are affected by their conversational history in unexpected ways. For instance, hallucinations in prior interactions may influence subsequent model responses. In this work, we introduce History-Echoes, a framework that investigates how conversational history biases subsequent generations. The framework explores this bias from two perspectives: probabilistically, we model conversations as Markov chains to quantify state consistency; geometrically, we measure the consistency of consecutive hidden representations. Across three model families and six datasets spanning diverse phenomena, our analysis reveals a strong correlation between the two perspectives. By bridging these perspectives, we demonstrate that behavioral persistence manifests as a geometric trap, where gaps in the latent space confine the model's trajectory. Code available at https://github.com/technion-cs-nlp/OldHabitsDieHard.
Executive Summary
This article presents a novel framework, History-Echoes, to investigate how the conversational history of large language models (LLMs) affects their future performance. By analyzing LLMs from both probabilistic and geometric perspectives, the authors demonstrate a strong correlation between the two approaches. Their findings suggest that conversational history can lead to behavioral persistence, manifesting as a geometric trap that confines the model's trajectory. This study contributes to our understanding of LLMs' limitations and potential biases. The authors' framework has implications for LLM development and deployment, particularly in applications where historical context is critical. The study's code is publicly available, facilitating further research and experimentation.
Key Points
- ▸ The article introduces History-Echoes, a framework to investigate conversational history's influence on LLMs.
- ▸ The framework explores conversational history from probabilistic and geometric perspectives.
- ▸ The study finds a strong correlation between the two approaches and identifies behavioral persistence as a geometric trap.
Merits
Strength in Methodology
The authors' use of a dual-perspective framework to investigate conversational history is a significant methodological contribution, providing a comprehensive understanding of LLMs' limitations.
Empirical Generalizability
The study's analysis of three model families and six datasets spanning diverse phenomena increases the generalizability of the findings, making them more applicable to real-world scenarios.
Demerits
Limitation in Scope
The study focuses primarily on LLMs' conversational history, neglecting other potential factors that may influence model performance, such as data quality or model architecture.
Potential for Overfitting
The authors' reliance on a specific set of datasets and models may lead to overfitting, limiting the study's broader applicability and generalizability.
Expert Commentary
This article makes a significant contribution to the field of natural language processing by shedding light on the conversational history's influence on LLMs. The authors' framework, History-Echoes, provides a novel and comprehensive approach to understanding LLMs' limitations. The study's findings have far-reaching implications for LLM development, deployment, and evaluation, particularly in applications where historical context is critical. However, the study's limitations, including its focus on conversational history and reliance on a specific set of datasets and models, should be carefully considered. Future research should aim to expand the scope of the study to include other potential factors influencing model performance and to develop more robust testing and evaluation protocols.
Recommendations
- ✓ Recommendation 1: Future studies should aim to replicate the study's findings across a broader range of datasets, models, and applications to increase the generalizability and robustness of the results.
- ✓ Recommendation 2: Researchers should explore the development of more robust testing and evaluation protocols to mitigate the potential for AI systems to perpetuate and amplify existing biases and limitations.