53 citations · 98 across the 11 of their papers we have counts for
14 papers
Effect of deep-trap density of states for a-Si:H layers on HIT cell performances: Modeling Framework
Tewfik Souier, Adel Gougam
In this work, an aSi and cSi heterojunction solar cell with an intrinsic layer is simulated using AFORS-HET software. The study focuses on the role of the electronic density of sta…
Code Monitor Red Teaming for Public-Test-Passing Code
Junchi Liao, Jiawen Deng, Fuji Ren
Visible tests are a common gate for LLM-generated code, but passing them does not certify specification correctness. We study a deployment-like monitoring problem: after code has p…
CodeClash: Benchmarking Goal-Oriented Software Engineering
John Yang, Kilian Lieret, Joyce Yang +6
Current benchmarks for coding evaluate language models (LMs) on concrete, well-specified tasks such as fixing specific bugs or writing targeted tests. However, human programmers do…
When Models Know More Than They Can Explain: Quantifying Knowledge Transfer in Human-AI Collaboration
Quan Shi, Carlos E. Jimenez, Shunyu Yao +3
Recent advancements in AI reasoning have driven substantial improvements across diverse tasks. A critical open question is whether these improvements also yields better knowledge t…
SWE-smith: Scaling Data for Software Engineering Agents
John Yang, Kilian Lieret, Carlos E. Jimenez +7
Despite recent progress in Language Models (LMs) for software engineering, collecting training data remains a significant pain point. Existing datasets are small, with at most 1,00…
IMPersona: Evaluating Individual Level LM Impersonation
Quan Shi, Carlos E. Jimenez, Stephen Dong +4
As language models achieve increasingly human-like capabilities in conversational text generation, a critical question emerges: to what extent can these systems simulate the charac…
SWE-bench Multimodal: Do AI Systems Generalize to Visual Software Domains?
John Yang, Carlos E. Jimenez, Alex L. Zhang +10
Autonomous systems for software engineering are now capable of fixing bugs and developing features. These systems are commonly evaluated on SWE-bench (Jimenez et al., 2024a), which…
EnIGMA: Interactive Tools Substantially Assist LM Agents in Finding Security Vulnerabilities
Talor Abramovich, Meet Udeshi, Minghao Shao +13
Although language model (LM) agents have demonstrated increased performance in multiple domains, including coding and web-browsing, their success in cybersecurity has been limited.…
SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering
John Yang, Carlos E. Jimenez, Alexander Wettig +4
Language model (LM) agents are increasingly being used to automate complicated tasks in digital environments. Just as humans benefit from powerful software applications, such as in…
SWE-bench: Can Language Models Resolve Real-World GitHub Issues?
Carlos E. Jimenez, John Yang, Alexander Wettig +4
Language models have outpaced our ability to evaluate them effectively, but for their future development it is essential to study the frontier of their capabilities. We find real-w…
C-STS: Conditional Semantic Textual Similarity
Ameet Deshpande, Carlos E. Jimenez, Howard Chen +6
Semantic textual similarity (STS), a cornerstone task in NLP, measures the degree of similarity between a pair of sentences, and has broad application in fields such as information…
MUX-PLMs: Data Multiplexing for High-throughput Language Models
Vishvak Murahari, Ameet Deshpande, Carlos E. Jimenez +4
The widespread adoption of large language models such as ChatGPT and Bard has led to unprecedented demand for these technologies. The burgeoning cost of inference for ever-increasi…
CARETS: A Consistency And Robustness Evaluative Test Suite for VQA
Carlos E. Jimenez, Olga Russakovsky, Karthik Narasimhan
We introduce CARETS, a systematic test suite to measure consistency and robustness of modern VQA models through a series of six fine-grained capability tests. In contrast to existi…
DataMUX: Data Multiplexing for Neural Networks
Vishvak Murahari, Carlos E. Jimenez, Runzhe Yang +1
In this paper, we introduce data multiplexing (DataMUX), a technique that enables deep neural networks to process multiple inputs simultaneously using a single compact representati…