LMSYS-Chat-1M: Large-Scale Real-World LLM Conversation Dataset

Core Concepts

The author introduces the LMSYS-Chat-1M dataset, highlighting its importance in understanding and advancing large language models (LLMs) through real-world conversations.

Abstract

The LMSYS-Chat-1M dataset is a large-scale collection of one million real-world conversations with 25 state-of-the-art LLMs. It offers insights into user interactions with LLMs and serves as a valuable resource for various applications, including content moderation, safety benchmarking, instruction-following model training, and creating challenging benchmark questions. The dataset addresses the need for diverse, real-user queries to enhance LLM capabilities.

The dataset was collected from an online LLM service hosting 25 popular models over five months. It contains conversations in multiple languages and covers various topics. The paper demonstrates the versatility of the dataset through four use cases and highlights its potential for future studies on LLM-user interactions.

Key points include:

• Introduction of the first large-scale real-world LLM conversation dataset.
• Analysis of basic statistics and topic distribution within the dataset.
• Use cases such as developing content moderation models, building safety benchmarks, training instruction-following models, and creating challenging benchmark questions.
• Comparison of model performance on different tasks using the dataset.
• Discussion on limitations and future work related to the dataset.

Stats

"Table 1: Basic statistics of several conversation datasets, including Anthropic HH (helpfulness and harmlessness) (Bai et al., 2022a), OpenAssistant Conversations (K¨opf et al., 2023), Chatbot Arena Conversations (Zheng et al., 2023), and LMSYS-Chat-1M. The tokens are counted by Llama2’s tokenizer. “Conv” = Conversation. “Lang” = Language."
"Table 2: The distribution of violation categories across all flagged conversations in LMSYS-Chat-1M."
"Table 3: Micro-F1 accuracy on 5-category content moderation task for different models."
"Table 4: Category distributions among all jailbreak conversations for representative LLMs."
"Table 5: Safety benchmark based on jailbreak conversations for several representative LLMs."
"Table 6: Evaluation results of instruction-following models on MMLU and MT-bench using subsets from LMSYS-Chat-1M."
"Figure 4: Score distribution by GPT-3.5-Turbo for evaluating prompts in creating challenging benchmark questions."
"Figure 5: Comparison between GPT-4 and GPT-3.5-Turbo performance on top-scored vs bottom-scored prompts."
"Figure 6: Model performance comparison on Arena-Hard-200 benchmark questions derived from Chatbot Arena."

Quotes

"While this figure represents the distribution of sampled conversations, it might not reflect the real-world topic distributions." - Zheng et al., ICLR Conference Paper
"We make the following contributions in this paper..." - Zheng et al., ICLR Conference Paper
"The lack of user registration and data filtering can result in a significant amount of low-quality and duplicate data." - Zheng et al., ICLR Conference Paper
"The majority of users are interested in trying and testing the latest LLMs." - Zheng et al., ICLR Conference Paper
"We encourage more research on evaluation methods using this dataset." - Zheng et al., ICLR Conference Paper

Key Insights Distilled From

LMSYS-Chat-1M

by Lianmin Zhen... at arxiv.org 03-12-2024

https://arxiv.org/pdf/2309.11998.pdf

Deeper Inquiries

How can biases in user distribution affect the generalizability of findings from this dataset?

Biases in user distribution can impact the generalizability of findings from this dataset by skewing the representation of different user groups. For example, if the majority of users interacting with LLMs are researchers or hobbyists, the conversations may not accurately reflect how everyday users or individuals from diverse professions interact with these models. This could lead to results that do not fully capture real-world interactions and limit the applicability of any insights gained.

What are some potential implications of having repeated or low-quality data within this dataset?

Having repeated or low-quality data within this dataset can introduce noise and reduce the overall quality and reliability of analyses conducted using it. Repeated data may inflate certain patterns or trends, leading to biased conclusions. Low-quality data, on the other hand, may introduce inaccuracies and inconsistencies that could impact model training or evaluation outcomes. It is essential to address these issues through proper filtering and preprocessing techniques to ensure robust research outcomes.

How might human preference annotations enhance the usability of this conversation dataset?

Human preference annotations can enhance the usability of this conversation dataset by providing valuable insights into user perceptions and preferences regarding LLM interactions. By incorporating human judgments on conversation quality, relevance, tone, etc., researchers can better understand which conversations are more engaging, informative, or respectful. These annotations can guide future studies on content moderation algorithms, model performance evaluations, and even help improve user experience in chatbot applications based on real-user feedback.