5 ms·
When LLM judges agree, should we believe them?
- bryzaguy 1mo agoThey would all agree raspberry has two Rs
- Joel_Mckay 1mo agoBut still refuse to answer "How many strings does a bass play with in water?" correctly, perhaps the chat monitors in the third world data entry centers will manually patch the nonsense for a more rational answer someday. lol =3
- wbobeirne 1mo agoThis was the first time I'd heard that gotcha question. I just threw it at Opus 5: None — a bass in water is a fish, and fish are notoriously bad at music. The instrument version plays four strings as standard (five and six-string basses exist for players who want to go lower or higher), and it prefers to stay dry. Seems like a pretty good answer to me!
- Joel_Mckay 1mo agoIndeed, giving a definite answer to an ambiguous nonsense question is still incorrect. A fish can play with as many strings as it finds, but only one when on a hook. Yet this too is an incorrect answer, as it again ignores the ambiguity in the phrasing. =3
- ChrisClark 1mo agoHow do you know the fish is playing? Is he happy, enjoying it? =3
- Joel_Mckay 1mo agoI agree, part of the ambiguity is also unfairly projecting our own subjective experience onto hapless creatures. =3
- aesthesia 1mo agoThis feels like an xkcd 169 situation, to be honest.
- Joel_Mckay 1mo agoIt was actually a trivial allusion to a rather old poetic parable, and highlights a foundational flaw in LLM inference model statistical salience. If a LLM based chat bot does ever answer it correctly, than you know with a fair degree of certainty it was content moderators stepping into the chat. Have a wonderful day. =3 https://en.wikisource.org/wiki/The_Poems_of_John_Godfrey_Saxe/The_Blind_Men_and_the_Elephant https://en.wikisource.org/wiki/The_Poems_of_John_Godfrey_Sax... https://en.wikipedia.org/wiki/Blind_men_and_an_elephant https://en.wikipedia.org/wiki/Blind_men_and_an_elephant
- aesthesia 1mo agoOK, I still have no idea what you actually think the "correct" answer is.
- quietbritishjim 1mo agoI think they want the correct answer to be "your question doesn't really make sense, so I'm not going to answer it". (But I also think Opus's answer is better.)
- Joel_Mckay 1mo agoAll the answers I've seen so far are assuredly not inaccurate (the elephants trunk is like a snake), but never fully correct (an elephant is not a snake). While the LLM spits out each ambiguous context search result, it never answers the actual query without a human cheaters help. =3 https://en.wikipedia.org/wiki/Pareidolia https://en.wikipedia.org/wiki/Pareidolia
- asamoahf 1mo ago[flagged]
- Forgeties79 1mo agothe LLM-speak is unbearable
- vips7L 1mo agoIt’s awful. A ton of words to say absolutely nothing.
- Forgeties79 1mo agoI do not understand why anyone would want an LLM to cosplay as them on a forum. I get maybe (selfish) idle curiosity but beyond that…?
- vips7L 1mo agoI don’t know why anyone just blatantly copies and paste LLM output. Laziness? Anxiety about being wrong? I don’t know.
- Forgeties79 1mo agoWell that’s corner cutting/avoiding work so I can see it. But commenting on forums?
- Tsarp 1mo agoKinda weird to generalize "LLM". Every lab, every model is different. Has its own biases, reward functions etc.
- Centigonal 1mo ago[dead]
- novaapi 1mo ago[dead]
- troupo 1mo agoWithout reading the article (doesn't matter if it's pro or contra): no, of course not. It shouldn't even be a debatable question.
- dgellow 1mo agoI think you should have read the article first, at minimum the subheader > Discounting the opinions of LLM judges with highly correlated outputs ensures that panels of judges reflect a true diversity of perspectives.
- troupo 1mo agoThe quoted sentence still leads to the same answer: no. Because there's no "discounting of opinions". They are running a separate LLM to "score" opinions. And the result is still "no" regardless of "lineages" or "sources". And the end of the article leads me to believe that the entire article and approach is LLM-induced garbage: --- start quote --- <Following a list of LLM-like suggestions> When LLM judges agree, we should ask why. Sometimes agreement is independent evidence. Sometimes it is a shared blind spot. A good aggregation method should be able to tell the difference. --- end quote ---
- pessimizer 1mo agoThis doesn't even make logical sense. Actual judges have highly correlated outputs. This would literally be actively sculpting the range of opinions you want to see. It's like the idea of political districting that thinks that the aim should be to balance each district between "the two" political parties. You're not doing anything but institutionalizing two political parties and constant conflict. You're setting the range of acceptable opinions, then choosing at random between them. Even more relevantly: when both institutionalized parties have the same opinion, it's considered the correct opinion no matter how much or how little public support it has.
- RugnirViking 1mo ago> the aim should be to balance each district between "the two" political parties. I've never heard anyone explicitly advocating for gerrymandering in favor of conflict/variance before? Is that a real thing?
- Founderarcstone 1mo agoGreat point this will be interesting how this develops.
- qarl 1mo agoWhile this is absolutely true - I'd hesitate to discount using similar agents for checking each other. Two agents will almost never hallucinate in the same way, regardless of their weights - and by having a second one (with a different context) check almost entirely eliminates the problem.
- emodendroket 1mo agoIt depends what we're judging, doesn't it? If it's "is the formatting in this document compliant with our standards?" I think it's reasonable. If it's like, life-altering if it's wrong I'm less sanguine.
- Joel_Mckay 1mo agoThey have already shown algorithmic discrimination in predicting recidivism for brown people, as they are nonsensically overrepresented in the statistical data of US prison populations. Folks should sue in a class-action lawsuit, any legal firm worth their beautiful walnut desks would seriously be happy take on that constitutionally backed mission. =3
- suburban_strike 1mo ago> brown people, as they are nonsensically overrepresented in the statistical data of US prison populations "Nonsensical?" They commit violent crimes, they get prosecuted for said violent crimes, and are serving prison sentences for those crimes. The algorithm picks up on this trend using the same logic that insurance actuaries use, which has also been largely neutered by critical theory. What even is the argument here-- they're all innocent? Cops are ignoring piles of dead white people and their white murderers to only go patrol brown neighborhoods? We both know neither claim is true. The usual complaint is that cops avoid their neighborhoods and/or are lazy in investigating the crimes they report. The idea of overpolicing has always been a Marxist double-bind...nonsensical, I daresay.
- qarl 1mo agoOh. Yes. Sure. If there is an error in the training data then they will obviously cite that error. I'm mostly talking about random coding errors.
- VaradD09 1mo agoI believe it depends on the LLM itself. Like what model as each model has diff weights and diff data trained onn
- dgellow 1mo agoI would recommend to read the article, it’s actually more nuanced than the title
- ex1fm3ta 1mo agoI kinda find it funny when I use the advisor on claude code and it agrees with the ideas that the previous model did. For info: the advisor(s) available are higher end models. For example: you use sonnet, the available advisors are opus and fable. If you use Haiku, the advisor are sonnet, opus and fable.
- mey 1mo agoA shared blind spot as noted at the bottom needs to be considered more often. In my day job, most of my coordination with others and now LLMs, is clarifying context and requirements. Claude is very happy to make assertions without the full picture in my experience, even when I give it as much context as I can.
- nekusar 1mo agoBetteridges law of headlines says "NO"
- sgt101 1mo ago3 tasks?
- peter_d_sherman 1mo agoObservation: A panel of Judges (multiple Judges), whether it's multiple AI's or not, is fundamentally -- a Jury!
- audelesauvage 1mo ago[dead]
- lhk931122 1mo agoSince the baseline LLM architecture is similar with each other, maybe already there are relations between their each opinion. Of course, this is an assumption and the explicit training seems effective in this case. But, I'm curious whether the approach would be effective in other cases (in terms of generalization?)