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Found 8 papers, 7 papers with code
A Call for New Recipes to Enhance Spatial Reasoning in MLLMs
Multimodal Large Language Models (MLLMs) have demonstrated impressive performance in general vision-language tasks.
Imagine while Reasoning in Space: Multimodal Visualization-of-Thought
Ultimately, MVoT establishes new possibilities for complex reasoning tasks where visual thinking can effectively complement verbal reasoning.
TopViewRS: Vision-Language Models as Top-View Spatial Reasoners
In this work, we thus study their capability to understand and reason over spatial relations from the top view.
Semantic Map-based Generation of Navigation Instructions
In this paper, we propose a new approach to navigation instruction generation by framing the problem as an image captioning task using semantic maps as visual input.
On Task Performance and Model Calibration with Supervised and Self-Ensembled In-Context Learning
Following the standard supervised fine-tuning (SFT) paradigm, in-context learning (ICL) has become an efficient approach propelled by the recent advancements in large language models (LLMs), yielding promising performance across various tasks in few-shot data setups.
Generating Data for Symbolic Language with Large Language Models
However, such an approach has primarily been applied to natural language tasks and has not yet been explored for symbolic language tasks with complex structured outputs (e. g., semantic parsing and code generation).
Binding Language Models in Symbolic Languages
We propose Binder, a training-free neural-symbolic framework that maps the task input to a program, which (1) allows binding a unified API of language model (LM) functionalities to a programming language (e. g., SQL, Python) to extend its grammar coverage and thus tackle more diverse questions, (2) adopts an LM as both the program parser and the underlying model called by the API during execution, and (3) requires only a few in-context exemplar annotations.
Ranked #5 on
Table-based Fact Verification
on TabFact
UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models
Structured knowledge grounding (SKG) leverages structured knowledge to complete user requests, such as semantic parsing over databases and question answering over knowledge bases.
Ranked #1 on
Task-Oriented Dialogue Systems
on KVRET
