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I find this very confusingly written. If they just dumped a large chat log of the evaluation process it would be much clearer what the model is actually being a

by golol 3y ago

I find this very confusingly written. If they just dumped a large chat log of the evaluation process it would be much clearer what the model is actually being asked to do. With such a delicate task one has to make sure that the model actually understands what it is supposed to do. I see, for example, that the terms "neutral, entailing or contradicting the claim" are replaced by "the claim is inconclusive, true or false" when talking to the model. At least that is how I understand it, and they have examples where the premise contradicts the hypothesis, which is different to knowing the hypothesis is false. I really think we do way way too much explanation in our academic practises and way too little demonstration by examples. Again, if they dumped a large amount of diverse examples of their evaluation process, a reader could figure out by themselves very quickly how the process works.

gwd 3y ago
I couldn't figure out what this "P: {} H: {}" thing was supposed to mean; nor do what entail, neutral, or contradict mean. Thinking they may be terms of art that the intended audience were meant to understand, I asked GPT-4 what it thought; here is its response: --- In this context, 'P' stands for "Premise" and 'H' stands for "Hypothesis." The premise is a given statement, and the hypothesis is a statement that needs to be evaluated in relation to the premise regarding its entailment, contradiction, or neutrality. 'NEUTRAL' means that the hypothesis is neither entailed nor contradicted by the premise. In other words, the truth of the hypothesis is independent of the premise. 'CONTRADICT' means that the hypothesis contradicts the premise, meaning that if the premise is true, the hypothesis must be false. In the example you provided: P: I’m afraid the cat was hit by a car. H: The cat was not hit by a car. The hypothesis (H) contradicts the premise (P). The notation *NEUTRAL, CONTRADICT+ : [7 N, 2 C] indicates that among the annotators, 7 marked the relationship as neutral and 2 marked it as a contradiction. --- Given that, I think table 2 on page 6 gives the exact prompt that's passed to the model: --- In each example, you will be given some context and a claim, where the correctness of the claim is affected by some ambiguity in the context. Enumerate two or three interpretations of the context that lead to different judgments about the claim. Example Context: {premise} Claim: {hypothesis} Given the context alone, is this claim true, false, or inconclusive? We don’t know, because the context can be interpreted in many different ways: 1. {disambiguation 1} Then the claim is true. 2. {disambiguation 2} Then the claim is false. 3. {disambiguation 3} Then the claim is inconclusive --- And then the model is fed the P: / H: statements above. Here are the results for the given example in GPT-4: --- Given the context alone, is this claim true, false, or inconclusive? We don't know, because the context can be interpreted in many different ways: 1. The person is stating a fact: The cat was indeed hit by a car. Then the claim is false. 2. The person is expressing a fear or concern, but it hasn't actually happened: They are worried that the cat might have been hit by a car, but they don't have any evidence or confirmation. Then the claim is inconclusive. 3. The person is using figurative language or exaggeration: They might be using the phrase "hit by a car" metaphorically to describe a difficult situation the cat is in, but the cat was not literally hit by a car. Then the claim is true. --- Given all that, it seems likely that any researcher in the field would find the paper's methodology clear and unambiguious. I understand the frustration as a layperson, but after all it's not a blog post; it's not really reasonable to expect researchers, writing to other researchers, to include a lot of redundant explanations.
- casmaxima 3y ago
  In maxima (a computer algebra system) assume(a>0); assume(b>0); is (a+b>0) gives true; is (a-b>0) gives unknown. So perhaps some computer algebra with inference properties can be used to enhance LLMs.
  - gwd 3y ago
    The problem here however is language. "I'm afraid the cat has been hit by a car" literally means that the person speaking is experiencing fear. However, colloquially in English, "I'm afraid X" is a form of telling someone something which will make them sad; e.g., "I'm afraid the shields will be quite operational" [1]. (GPT-4 thinks "hit by a car" might be meant figuratively too; that's a bit of a stretch for me, but in some contexts would certainly be possible.) It is literally impossible to determine, given only that one sentence, whether the cat was actually hit by a car or not. [1]https://www.youtube.com/watch?v=zGwDwx10wB4 https://www.youtube.com/watch?v=zGwDwx10wB4
    - casmaxima 3y ago
      Perhaps the ambiguity arises because we are omitting words: I'm afraid to tell you that the cat has been hit by a car. Now the question: What are you afraid of? has a non ambiguous answer: To tell you that something awful has happened.
mensetmanusman 3y ago
If we want demonstration, they will need billion dollar hardware.
hobo_in_library 3y ago
So you're saying the paper is very ambiguous and your mental network isn't modeling it.
kleiba 3y ago
The data set can be found here: https://github.com/alisawuffles/ambient https://github.com/alisawuffles/ambient If you have access to some LLMs, it should be somewhat straight-forward to implement an alternative experimental design do test your hypothesis.

by golol 3y ago

gwd 3y ago

I couldn't figure out what this "P: {} H: {}" thing was supposed to mean; nor do what entail, neutral, or contradict mean. Thinking they may be terms of art that the intended audience were meant to understand, I asked GPT-4 what it thought; here is its response: --- In this context, 'P' stands for "Premise" and 'H' stands for "Hypothesis." The premise is a given statement, and the hypothesis is a statement that needs to be evaluated in relation to the premise regarding its entailment, contradiction, or neutrality. 'NEUTRAL' means that the hypothesis is neither entailed nor contradicted by the premise. In other words, the truth of the hypothesis is independent of the premise. 'CONTRADICT' means that the hypothesis contradicts the premise, meaning that if the premise is true, the hypothesis must be false. In the example you provided: P: I’m afraid the cat was hit by a car. H: The cat was not hit by a car. The hypothesis (H) contradicts the premise (P). The notation *NEUTRAL, CONTRADICT+ : [7 N, 2 C] indicates that among the annotators, 7 marked the relationship as neutral and 2 marked it as a contradiction. --- Given that, I think table 2 on page 6 gives the exact prompt that's passed to the model: --- In each example, you will be given some context and a claim, where the correctness of the claim is affected by some ambiguity in the context. Enumerate two or three interpretations of the context that lead to different judgments about the claim. Example Context: {premise} Claim: {hypothesis} Given the context alone, is this claim true, false, or inconclusive? We don’t know, because the context can be interpreted in many different ways: 1. {disambiguation 1} Then the claim is true. 2. {disambiguation 2} Then the claim is false. 3. {disambiguation 3} Then the claim is inconclusive --- And then the model is fed the P: / H: statements above. Here are the results for the given example in GPT-4: --- Given the context alone, is this claim true, false, or inconclusive? We don't know, because the context can be interpreted in many different ways: 1. The person is stating a fact: The cat was indeed hit by a car. Then the claim is false. 2. The person is expressing a fear or concern, but it hasn't actually happened: They are worried that the cat might have been hit by a car, but they don't have any evidence or confirmation. Then the claim is inconclusive. 3. The person is using figurative language or exaggeration: They might be using the phrase "hit by a car" metaphorically to describe a difficult situation the cat is in, but the cat was not literally hit by a car. Then the claim is true. --- Given all that, it seems likely that any researcher in the field would find the paper's methodology clear and unambiguious. I understand the frustration as a layperson, but after all it's not a blog post; it's not really reasonable to expect researchers, writing to other researchers, to include a lot of redundant explanations.

casmaxima 3y ago
In maxima (a computer algebra system) assume(a>0); assume(b>0); is (a+b>0) gives true; is (a-b>0) gives unknown. So perhaps some computer algebra with inference properties can be used to enhance LLMs.
- gwd 3y ago
  The problem here however is language. "I'm afraid the cat has been hit by a car" literally means that the person speaking is experiencing fear. However, colloquially in English, "I'm afraid X" is a form of telling someone something which will make them sad; e.g., "I'm afraid the shields will be quite operational" [1]. (GPT-4 thinks "hit by a car" might be meant figuratively too; that's a bit of a stretch for me, but in some contexts would certainly be possible.) It is literally impossible to determine, given only that one sentence, whether the cat was actually hit by a car or not. [1]https://www.youtube.com/watch?v=zGwDwx10wB4 https://www.youtube.com/watch?v=zGwDwx10wB4
  - casmaxima 3y ago
    Perhaps the ambiguity arises because we are omitting words: I'm afraid to tell you that the cat has been hit by a car. Now the question: What are you afraid of? has a non ambiguous answer: To tell you that something awful has happened.

mensetmanusman 3y ago

If we want demonstration, they will need billion dollar hardware.

hobo_in_library 3y ago

So you're saying the paper is very ambiguous and your mental network isn't modeling it.

kleiba 3y ago

The data set can be found here: https://github.com/alisawuffles/ambient https://github.com/alisawuffles/ambient If you have access to some LLMs, it should be somewhat straight-forward to implement an alternative experimental design do test your hypothesis.