Danny To Eun Kim

@teknology.bsky.social

PhD student @CMU LTI NLP | IR | Evaluation | RAG https://kimdanny.github.io

#chatGPT began to put ads in their response. Check out our paper on “Ads detection and integration in the era of LLMs”. ceur-ws.org/Vol-4038/pap...

ceur-ws.org

Danny To Eun Kim@teknology.bsky.social · 11mo ago

Excited to present at #CLEF2025 #Touché Lab (Session 2) shared task "Advertisement in RAG"🇪🇸! @webis.de 🗓️Sept 9 (Tue) ⏲️5:20PM (CEST) / 11:20AM (EST) 📍Florentino Sanz Room 🧠https://arxiv.org/abs/2507.00509 Join us for insights on #RAG + advertising!

advertisement generation and detection in RAG

Some exciting news! 🤗 After 3 amazing years at TREC, the Tip-of-the-Tongue (ToT) shared task will be a core task at NTCIR-19 in 2026. The new track will focus on tip-of-the-tongue information needs in English and East Asian languages. More details coming soon. See you all in Tokyo next year!

an aerial view of tokyo at night with lots of lights

ALT: an aerial view of tokyo at night with lots of lights

media.tenor.com

This year's TREC Tip of the Tongue (ToT) track will be amazing! Based on our rigorous experiments on synthetic ToT query generation presented at #SIGIR2025, we extended the track to open domain ToT queries. We provide codes for baseline systems, and submissions are due by August 27th!

Bhaskar Mitra | ভাস্কর মিত্র@bmitra.bsky.social · last yr.

Important announcement: All run submissions for TREC'25 Tip-of-the-Tongue (TREC-ToT) Track are due by **August 27th**. The run submission form is now open. Please submit your runs before the deadline. More information: trec-tot.github.io/guidelines #TREC2025 #TRECToT #TREC2025ToT Spread the word!

❓How do LLMs respond to fair ranking in RAG? 🤩 See how fair ranking boosts downstream utility while promoting fairer attribution of cited sources. Catch our oral presentation at #ICTIR2025! #SIGIR2025 @841io.bsky.social

Danny To Eun Kim@teknology.bsky.social · 2y ago

Heading to #NeurIPS2024 to present our ‘Fair RAG’ paper at the #AFME2024 workshop! Let's talk about RAG, Information Retrieval, and Fairness. Honored that our paper was selected as one of the Top 5 Spotlight Papers! 🎉 Let’s connect and chat! Paper: arxiv.org/abs/2409.11598

🖋️ Curious how writing differs across (research) cultures? 🚩 Tired of “cultural” evals that don't consult people? We engaged with interdisciplinary researchers to identify & measure ✨cultural norms✨in scientific writing, and show that❗LLMs flatten them❗ 📜 arxiv.org/abs/2506.00784 [1/11]

An overview of the work “Research Borderlands: Analysing Writing Across Research Cultures” by Shaily Bhatt, Tal August, and Maria Antoniak. The overview describes that We  survey and interview interdisciplinary researchers (§3) to develop a framework of writing norms that vary across research cultures (§4) and operationalise them using computational metrics (§5). We then use this evaluation suite for two large-scale quantitative analyses: (a) surfacing variations in writing across 11 communities (§6); (b) evaluating the cultural competence of LLMs when adapting writing from one community to another (§7).

Hello TREC-ToTers! 👋🏽 Excited to announce the release of TREC 2025 Tip-of-the-Tongue (TREC-ToT) Track guidelines: trec-tot.github.io/guidelines. We will release test queries in July and run submission deadline will be in August. #TREC2025 #TRECToT #TREC2025ToT Please register to participate:

TREC 2025 Tip-of-the-Tongue (ToT) Track

Tip of the tongue: The phenomenon of failing to retrieve something from memory, combined with partial recall and the feeling that retrieval is imminent.

trec-tot.github.io

🚨New Breakthrough in Tip-of-the-Tongue (TOT) Retrieval Research! We address data limitations and offer a fresh evaluation method for these complex queries. Curious how TREC TOT track test queries are created? Check out this thread 🧵 and our paper 📄: arxiv.org/abs/2502.17776

Tip of the Tongue Query Elicitation for Simulated Evaluation

Tip-of-the-tongue (TOT) search occurs when a user struggles to recall a specific identifier, such as a document title. While common, existing search systems often fail to effectively support TOT scena...

arxiv.org

Mat is not on 🦋—posting on his behalf! It's time to revisit common assumptions in IR! Embeddings have improved drastically, but mainstream IR evals have stagnated since MSMARCO + BEIR. We ask: on private or tricky IR tasks, are rerankers better? Surely, reranking many docs is best?

A plot showing that reranking improves recall as we increase the number of reranked docs, but with increasing docs we diminishing returns and eventually a performance dip.