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Found 16 papers, 9 papers with code
Few-shot Policy (de)composition in Conversational Question Answering
The task of policy compliance detection (PCD) is to determine if a scenario is in compliance with respect to a set of written policies.
NESTFUL: A Benchmark for Evaluating LLMs on Nested Sequences of API Calls
Research on tool calling has gathered momentum, but evaluation benchmarks and datasets representing the complexity of the tasks have lagged behind.
Granite Code Models: A Family of Open Foundation Models for Code Intelligence
Increasingly, code LLMs are being integrated into software development environments to improve the productivity of human programmers, and LLM-based agents are beginning to show promise for handling complex tasks autonomously.
API-BLEND: A Comprehensive Corpora for Training and Benchmarking API LLMs
There is a growing need for Large Language Models (LLMs) to effectively use tools and external Application Programming Interfaces (APIs) to plan and complete tasks.
Formally Specifying the High-Level Behavior of LLM-Based Agents
We demonstrate how the proposed framework can be used to implement recent LLM-based agents (e. g., ReACT), and show how the flexibility of our approach can be leveraged to define a new agent with more complex behavior, the Plan-Act-Summarize-Solve (PASS) agent.
Compositional Program Generation for Few-Shot Systematic Generalization
Compositional generalization is a key ability of humans that enables us to learn new concepts from only a handful examples.
MISMATCH: Fine-grained Evaluation of Machine-generated Text with Mismatch Error Types
In this work, we propose a new evaluation scheme to model human judgments in 7 NLP tasks, based on the fine-grained mismatches between a pair of texts.
Scalable Learning of Latent Language Structure With Logical Offline Cycle Consistency
We introduce Logical Offline Cycle Consistency Optimization (LOCCO), a scalable, semi-supervised method for training a neural semantic parser.
An Ensemble Approach for Automated Theorem Proving Based on Efficient Name Invariant Graph Neural Representations
NIAGRA addresses this problem by using 1) improved Graph Neural Networks for learning name-invariant formula representations that is tailored for their unique characteristics and 2) an efficient ensemble approach for automated theorem proving.
Laziness Is a Virtue When It Comes to Compositionality in Neural Semantic Parsing
Nearly all general-purpose neural semantic parsers generate logical forms in a strictly top-down autoregressive fashion.
Learning to Guide a Saturation-Based Theorem Prover
In addition, to the best of our knowledge, TRAIL is the first reinforcement learning-based approach to exceed the performance of a state-of-the-art traditional theorem prover on a standard theorem proving benchmark (solving up to 17% more problems).
An Experimental Study of Formula Embeddings for Automated Theorem Proving in First-Order Logic
Automated theorem proving in first-order logic is an active research area which is successfully supported by machine learning.
Improving Graph Neural Network Representations of Logical Formulae with Subgraph Pooling
Recent advances in the integration of deep learning with automated theorem proving have centered around the representation of logical formulae as inputs to deep learning systems.
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Automated Theorem Proving
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A Deep Reinforcement Learning Approach to First-Order Logic Theorem Proving
Automated theorem provers have traditionally relied on manually tuned heuristics to guide how they perform proof search.
High-Fidelity Vector Space Models of Structured Data
Machine learning systems regularly deal with structured data in real-world applications.
BIG-bench Machine Learning
Vocal Bursts Intensity Prediction
