Agent-FLAN: Designing Data and Methods for Effective Agent Tuning in Large Language Models

Główne pojęcia

Agent-FLAN proposes effective fine-tuning methods for integrating agent abilities into large language models, outperforming prior works by 3.5% across various agent evaluation datasets.

Streszczenie

1. Abstract:

Large Language Models (LLMs) excel in NLP tasks but lag behind API-based models as agents.
Agent-FLAN addresses the integration of agent abilities into LLMs effectively.
2. Introduction:

Language agents leverage LLMs for real-world problem-solving.
Open-sourced LLMs show promise but struggle as agents compared to API-based models.
3. Pilot Observations:

Agent training data entangled with format following and general reasoning.
Decomposing training data reveals varied learning speeds on essential capabilities for agent tasks.
Existing approaches overlook hallucination issues in model output.

4. Agent-FLAN:

Aligns agent tuning to pretrain domain, decomposes capabilities, and balances data effectively.
5. Analysis:

Scaling law analysis shows the impact of data and model scales on agent tuning performance.
Agent tuning enhances both agent-specific and general capabilities of language models.

6. Conclusion:

Agent-FLAN offers insights into effective agent tuning methodologies for LLMs.

Statystyki

Open-sourced Large Language Models have achieved success in NLP tasks but lag behind API-based models as agents.
Agent-FLAN outperforms prior works by 3.5% across various agent evaluation datasets.

Cytaty

"Most agent training data is entangled with both format following and general reasoning."
"AgentTuning introduces mixture training, leading to steady performance improvements."

Kluczowe wnioski z

Agent-FLAN

by Zehui Chen,K... o arxiv.org 03-20-2024

https://arxiv.org/pdf/2403.12881.pdf

Głębsze pytania

How can the findings of Agent-FLAN be applied to improve other types of language models

Agent-FLAN's findings can be applied to improve other types of language models by focusing on effective agent tuning strategies. By aligning the training corpus to the natural conversation domain, decomposing data based on different capabilities, and introducing negative sample learning for hallucination elimination, these approaches can enhance the performance of various language models. For instance, incorporating similar techniques in fine-tuning processes for different tasks or domains could help optimize model performance and address specific challenges faced by those models.

What are the potential drawbacks or limitations of focusing on specific agent abilities in LLMs

Focusing solely on specific agent abilities in LLMs may have potential drawbacks or limitations. One limitation is that it might lead to overfitting on certain tasks or formats, limiting the model's adaptability and generalizability across a wide range of scenarios. Additionally, concentrating only on specialized agent abilities could neglect other essential skills that are crucial for comprehensive language understanding and task completion. This narrow focus may hinder the model's overall performance in diverse applications where a broader set of capabilities is required.

How can hallucination issues be addressed more effectively in language model outputs

To address hallucination issues more effectively in language model outputs, several strategies can be implemented:

Diverse Negative Sample Learning: Introduce a variety of negative samples during training to expose the model to different scenarios where hallucinations commonly occur.
Explicit Supervision: Provide explicit supervision during training to teach the model when not to generate hallucinatory responses.
Format-Level Checks: Implement format-level checks during inference to ensure that responses adhere strictly to specified formats without deviating into hallucinations.
Action-Level Validation: Curate datasets with induced questions covering various perspectives common in agent tasks and evaluate responses against expected actions.
Continuous Monitoring: Regularly monitor output responses for signs of hallucination and refine training strategies accordingly based on identified patterns.

By combining these approaches with rigorous evaluation benchmarks like Agent-H introduced by Agent-FLAN, language models can significantly reduce instances of hallucination in their outputs while maintaining high performance levels across diverse tasks and domains.