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Found 12 papers, 10 papers with code
LLM Pretraining with Continuous Concepts
We propose Continuous Concept Mixing (CoCoMix), a novel pretraining framework that combines discrete next token prediction with continuous concepts.
Linear Correlation in LM's Compositional Generalization and Hallucination
The generalization of language models (LMs) is undergoing active debates, contrasting their potential for general intelligence with their struggles with basic knowledge composition (e. g., reverse/transition curse).
Offline Reinforcement Learning for LLM Multi-Step Reasoning
While Direct Preference Optimization (DPO) has shown promise in aligning LLMs with human preferences, it is less suitable for multi-step reasoning tasks because (1) DPO relies on paired preference data, which is not readily available for multi-step reasoning tasks, and (2) it treats all tokens uniformly, making it ineffective for credit assignment in multi-step reasoning tasks, which often come with sparse reward.
Training Large Language Models to Reason in a Continuous Latent Space
For example, most word tokens are primarily for textual coherence and not essential for reasoning, while some critical tokens require complex planning and pose huge challenges to LLMs.
Pandora: Towards General World Model with Natural Language Actions and Video States
This paper makes a step towards building a general world model by introducing Pandora, a hybrid autoregressive-diffusion model that simulates world states by generating videos and allows real-time control with free-text actions.
LLM Reasoners: New Evaluation, Library, and Analysis of Step-by-Step Reasoning with Large Language Models
(2) We develop LLM Reasoners, a library for standardized modular implementation of existing and new reasoning algorithms, under a unified formulation of the search, reward, and world model components.
Reasoning with Language Model is Planning with World Model
RAP on LLAMA-33B surpasses CoT on GPT-4 with 33% relative improvement in a plan generation setting.
ToolkenGPT: Augmenting Frozen Language Models with Massive Tools via Tool Embeddings
Augmenting large language models (LLMs) with external tools has emerged as a promising approach to solving complex problems.
BertNet: Harvesting Knowledge Graphs with Arbitrary Relations from Pretrained Language Models
The resulting KGs as a symbolic interpretation of the source LMs also reveal new insights into the LMs' knowledge capacities.
Does Recommend-Revise Produce Reliable Annotations? An Analysis on Missing Instances in DocRED
DocRED is a widely used dataset for document-level relation extraction.
Benchmarking Commonsense Knowledge Base Population with an Effective Evaluation Dataset
Experimental results show that generalizing commonsense reasoning on unseen assertions is inherently a hard task.
