A surprising result from Databricks when measuring embeddings and rerankers on internal evals.
1- Reranking few docs improves recall (expected).
2- Reranking many docs degrades quality (!).
3- Reranking too many documents is quite often worse than using embedding model alone (!!).
A surprising result from Databricks when measuring embeddings and rerankers on internal evals.
1- Reranking few docs improves recall (expected).
2- Reranking many docs degrades quality (!).
3- Reranking too many documents is quite often worse than using embedding model alone (!!).
truly the pinnacle of an early fall weekend
truly the pinnacle of an early fall weekend
We add an LLM-powered reranking of highly polarizing political content into N=1256 participants' feeds. Downranking cools tensions with the opposite party—but upranking inflames them.
We add an LLM-powered reranking of highly polarizing political content into N=1256 participants' feeds. Downranking cools tensions with the opposite party—but upranking inflames them.
We ran a field experiment on X/Twitter (N=1,256) using LLMs to rerank content in real-time, adjusting exposure to polarizing posts. Result: Algorithmic ranking impacts feelings toward the political outgroup! 🧵⬇️
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?
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?
1. Cheese
2. Fish
3. Rumor
4. Iron
5. War
6. Conspiracy
An Easy-to-use Framework of Utilizing Large Language Models for Document Reranking.
Repo: github.com/liuqi6777/ll...
An Easy-to-use Framework of Utilizing Large Language Models for Document Reranking.
Repo: github.com/liuqi6777/ll...
Key from @jennyallen.bsky.social @jatucker.bsky.social: (1) important new paradigm (2) but not clear why results differ
www.science.org/doi/full/10....
Key from @jennyallen.bsky.social @jatucker.bsky.social: (1) important new paradigm (2) but not clear why results differ
www.science.org/doi/full/10....
Also featuring a modular CrossEncoder, and automatic Flash Attention 2 input flattening.
Highlights in 🧵
Also featuring a modular CrossEncoder, and automatic Flash Attention 2 input flattening.
Highlights in 🧵
#OpenSource Apache 2.0 license.
#AI #LLM #Reranking
GitHub github.com/NandhaKishor...
#OpenSource Apache 2.0 license.
#AI #LLM #Reranking
GitHub github.com/NandhaKishor...
The sexiest thing is bringing me on to facilitate reviewer relationships, generate value, and champion authors.
The sexiest thing is bringing me on to facilitate reviewer relationships, generate value, and champion authors.
"Reranking partisan animosity in algorithmic social media feeds alters affective polarization"
www.science.org/doi/10.1126/...
Led by @tiziano.bsky.social and @msaveski.bsky.social
"Reranking partisan animosity in algorithmic social media feeds alters affective polarization"
www.science.org/doi/10.1126/...
Led by @tiziano.bsky.social and @msaveski.bsky.social
1- Reranking few docs improves recall (expected).
2- Reranking many docs degrades quality (!).
3- Reranking too many documents is quite often worse than using embedding model alone (!!).
1- Reranking few docs improves recall (expected).
2- Reranking many docs degrades quality (!).
3- Reranking too many documents is quite often worse than using embedding model alone (!!).
1. Rachel
2. Sam
3. Sue
4. Teeny
5. Genevieve
6. Andy
1. Rachel
2. Sam
3. Andy
4. Genevieve
5. Sue
6. Kyle
7. Caroline
8. Teeny
1. Rachel
2. Sam
3. Sue
4. Teeny
5. Genevieve
6. Andy
These researchers rerouted the algorithm on Twitter to push some users toward “antidemocratic attitudes and partisan animosity”.
It only took 1 week to elicit changes that used to take 3 years.
These researchers rerouted the algorithm on Twitter to push some users toward “antidemocratic attitudes and partisan animosity”.
It only took 1 week to elicit changes that used to take 3 years.
"Up-ranking increased political polarization, whereas down-ranking decreased it."
Study: Reranking partisan animosity in algorithms alters polarization www.science.org/doi/10.1126/...
"Up-ranking increased political polarization, whereas down-ranking decreased it."
Study: Reranking partisan animosity in algorithms alters polarization www.science.org/doi/10.1126/...
Eine neue Studie hat nun selbst gemessen, dass ein Algorithmus, der „antidemokratische Einstellungen und parteipolitische Feindseligkeit“ im Feed stark einblendet, die Polarisierung antreibt
Siehe:
Eine neue Studie hat nun selbst gemessen, dass ein Algorithmus, der „antidemokratische Einstellungen und parteipolitische Feindseligkeit“ im Feed stark einblendet, die Polarisierung antreibt
Siehe:
You don’t need a violent coup or a hacked voting machines to overthrow democracy.
You just need control of the algorithm.
You don’t need a violent coup or a hacked voting machines to overthrow democracy.
You just need control of the algorithm.