Search Results for author:
Found 12 papers, 11 papers with code
APIGen-MT: Agentic Pipeline for Multi-Turn Data Generation via Simulated Agent-Human Interplay
We open-source 5K synthetic data trajectories and the trained xLAM-2-fc-r models to advance research in AI agents.
ActionStudio: A Lightweight Framework for Data and Training of Large Action Models
However, training large action models remains challenging due to the diversity of agent environments and the complexity of agentic data.
SpecTool: A Benchmark for Characterizing Errors in Tool-Use LLMs
To solve this problem, we introduce SpecTool, a new benchmark to identify error patterns in LLM output on tool-use tasks.
Language Models are Hidden Reasoners: Unlocking Latent Reasoning Capabilities via Self-Rewarding
Large language models (LLMs) have shown impressive capabilities, but still struggle with complex reasoning tasks requiring multiple steps.
CRMArena: Understanding the Capacity of LLM Agents to Perform Professional CRM Tasks in Realistic Environments
Customer Relationship Management (CRM) systems are vital for modern enterprises, providing a foundation for managing customer interactions and data.
LoRA Soups: Merging LoRAs for Practical Skill Composition Tasks
We study how different LoRA modules can be merged to achieve skill composition -- testing the performance of the merged model on a target task that involves combining multiple skills, each skill coming from a single LoRA.
xLAM: A Family of Large Action Models to Empower AI Agent Systems
By releasing the xLAM series, we aim to advance the performance of open-source LLMs for autonomous AI agents, potentially accelerating progress and democratizing access to high-performance models for agent tasks.
Deciphering the Factors Influencing the Efficacy of Chain-of-Thought: Probability, Memorization, and Noisy Reasoning
By focusing on a single relatively simple task, we are able to identify three factors that systematically affect CoT performance: the probability of the task's expected output (probability), what the model has implicitly learned during pre-training (memorization), and the number of intermediate operations involved in reasoning (noisy reasoning).
InterCode: Standardizing and Benchmarking Interactive Coding with Execution Feedback
Our framework is language and platform agnostic, uses self-contained Docker environments to provide safe and reproducible execution, and is compatible out-of-the-box with traditional seq2seq coding methods, while enabling the development of new methods for interactive code generation.
Commonsense and Named Entity Aware Knowledge Grounded Dialogue Generation
In this paper, we present a novel open-domain dialogue generation model which effectively utilizes the large-scale commonsense and named entity based knowledge in addition to the unstructured topic-specific knowledge associated with each utterance.
CL-NERIL: A Cross-Lingual Model for NER in Indian Languages
We employ a variant of the Teacher-Student model and optimize it jointly on the pseudo labels of the Teacher model and predictions on the generated weakly labeled data.
