9 papers
SemRF: A Semantic Reference Frame for Residual-Stream Dynamics in Language Models
Jian Gu, Aldeida Aleti, Chunyang Chen +1
Residual-stream analysis asks how language-model computation evolves across depth, but intermediate decoding requires comparable readout coordinates across layers. If embedding anc…
ViBR: Automated Bug Replay from Video-based Reports using Vision-Language Models
Sidong Feng, Dingbang Wang, Nikola Tomic +3
Bug reports play a critical role in software maintenance by helping users convey encountered issues to developers. Recently, GUI screen capture videos have gained popularity as a b…
Rethinking Weight Tying: Pseudo-Inverse Tying for LM Stable Training and Updates
Jian Gu, Aldeida Aleti, Chunyang Chen +1
Weight tying is widely used in compact language models to reduce parameters by sharing the token table between the input embedding and the output projection. However, parameter sha…
Beyond Neural Incompatibility: Cross-Scale Knowledge Transfer in Language Models through Latent Semantic Alignment
Jian Gu, Aldeida Aleti, Chunyang Chen +1
Language Models (LMs) encode substantial knowledge in their parameters, yet it remains unclear how to transfer such knowledge in a fine-grained manner, namely parametric knowledge…
SeMe: Training-Free Language Model Merging via Semantic Alignment
Jian Gu, Aldeida Aleti, Chunyang Chen +1
Despite the remarkable capabilities of Language Models (LMs) across diverse tasks, no single model consistently outperforms others, necessitating efficient methods to combine their…
A Semantic-based Optimization Approach for Repairing LLMs: Case Study on Code Generation
Jian Gu, Aldeida Aleti, Chunyang Chen +1
Language Models (LMs) are widely used in software engineering for code generation, but they may produce erroneous code. Rather than repairing outputs, a more thorough remedy is to…
A Semantic-Aware Layer-Freezing Approach to Computation-Efficient Fine-Tuning of Language Models
Jian Gu, Aldeida Aleti, Chunyang Chen +1
Finetuning language models (LMs) is crucial for adapting the models to downstream data and tasks. However, full finetuning is usually costly. Existing work, such as parameter-effic…
Vocabulary-Defined Semantics: Latent Space Clustering for Improving In-Context Learning
Jian Gu, Aldeida Aleti, Chunyang Chen +1
In-context learning enables language models (LM) to adapt to downstream data or tasks by incorporating few samples as demonstrations within the prompts. It offers strong performanc…
Neuron Patching: Semantic-based Neuron-level Language Model Repair for Code Generation
Jian Gu, Aldeida Aleti, Chunyang Chen +1
Language Models (LMs) have become widely used in software engineering, especially for tasks such as code generation, where they are referred to as code LMs. These models have prove…